Increasing the uptake of preexposure prophylaxis (PrEP) [1-3] is a key strategy in the Ending the HIV Epidemic (EHE) initiative [4]. However, social and structural barriers to HIV treatment-seeking behaviors continue to impact health outcomes and drive inequities [5-7]. PrEP adoption has been hindered in the United States by lack of PrEP knowledge and prevailing anticipated and experienced stigma and discrimination [5,7-9]. Personal experiences shared by peers can improve the awareness [10,11], self-efficacy, and intrinsic motivation [12] of people considering PrEP use, mitigating their stigmatized beliefs [10], and informing health decisions [10,13]. People without financial means or ready access to providers experience limited access to health care [14,15]. Service deserts result in a lack of physically proximate service providers [6]; scarcity of services also disproportionately impacts those without financial means or access [16]. Equitable PrEP uptake requires facilitating access to PrEP resources while meeting the diverse needs of PrEP candidates [16]. Conversational agents (ie, chatbots) have the potential to provide anonymous, round-the-clock assistance in finding a PrEP provider, answering questions about PrEP, and providing emotional support. These services can help to overcome geographical and access barriers to seeking PrEP care.

Integrating chatbots into existing public health programs can significantly mitigate treatment barriers while enhancing user trust and adoption. However, to ensure scalability, deployment must account for infrastructure constraints and linguistic diversity. In low-resource settings where internet access, device affordability, and digital literacy may be limited [17], chatbots should be designed for local deployment to ensure data privacy and accessibility. This includes supporting offline functionality for mobile devices and using lightweight architectures to ensure accessibility on older hardware. Furthermore, to cater to cultural and behavioral preferences, it is essential to incorporate multilingual and multiplatform support. Providing such user-centric personalization is a vital step in reducing health disparities and ensuring that digital interventions are both accessible and equitable.

Chatbots have demonstrated potential in disseminating HIV information [18,19], self-testing [18,20,21], access to and uptake of HIV prevention and care [22-24], and making positive behavioral changes relevant to HIV prevention and care (eg, self-disclosure of HIV status [25,26] and self-management of PrEP adherence [18,22,27]). AI technologies can be useful in developing and operating chatbot services, but their contribution is variable depending on the risk and complexity associated with health infrastructure. This is because risk and complexity influence the adoption of chatbots by users for improving health conditions [28]. Rule-based chatbots, which use scripted text based on simple rules (ie, keyword identification and pattern-matching techniques), are most commonly used to provide information [19,29] and support self-management of health behaviors (eg, setting up reminders) [22,25]. More advanced chatbots use information retrieval systems [23] and knowledge graphs [21] to help users identify HIV risk and navigate relevant information to mitigate barriers to PrEP uptake. For more complex needs such as identifying disorders and providing behavioral support (eg, for PrEP retention), chatbots use hybrid techniques that use AI tools (eg, machine learning [20,21,23] and natural language processing [21]) to learn patterns from previous conversations [30]. These approaches can also detect emotions [30] and perceive user characteristics based on past interactions [21], and can integrate dialogue rules for response generation. Two protocols for studies mention the use of a hybrid large language model (LLM; HumanX) chatbot for promoting PrEP uptake and use [24,27] and for providing HIV-related mental health support [22]. However, these studies do not specify details of the chatbot implementation. Such rule-based and hybrid health care chatbots are often criticized for their limited flexibility [31,32] and diversity of content [31-36], and insufficient contextual understanding [37,38] and personalization [23,39]. Such chatbots have also been reported to lack human-like emotional support [40], and this limits long-term user engagement and chatbot effectiveness [41]. LLMs, leveraging vast knowledge bases, can generate responses that are varied and context-relevant compared to rule-based and hybrid systems [42]. Beyond information retrieval, LLMs have demonstrated a significant capacity for cognitive empathy, the intellectual ability to identify and label a user’s emotional state and respond with supportive language [43,44]. However, researchers have noted that these responses often lack affective empathy [44], as the models lack human-like emotional experience and intrinsic motivation [43-45], leading to impersonal responses in sensitive and stigma-associated contexts [45]. This empathy gap suggests that while LLMs can extend general warmth, they struggle to provide the deep, personalized emotional connection found in human-to-human emotional support (ie, human-like emotional support).

To date, no study details the implementation of an LLM-based chatbot for providing various HIV and PrEP support, including emotional, informational, and peer experiential support to facilitate access to PrEP information and promote PrEP uptake and adherence. Although LLMs can offer improved personalization and human-like conversations [42,46], their performance can also be limited by their scope of specialized knowledge, diversity of available data, and the potential for LLM hallucinations [47,48]. Fine-tuned LLMs have been effective in performing domain-specific tasks; however, the small size of the training corpus limits their ability to generalize model capabilities across diverse user needs [31]. Prompt engineering [49] and retrieval-augmented generation (RAG) [48] techniques improve LLMs’ performance in terms of accuracy [50,51], relevancy [50,52], data privacy [53], and user personalization [54,55], which can enhance user trust in chatbot-provided support [56-58]. Using prompt engineering, LLMs can be guided to improve the precision and accuracy of generated responses [57]. RAG chatbots have indicated improved accuracy (34%) [49] and reduced hallucination (26.5%) [48] in generated responses compared to LLM question-answering bots. Compared to fine-tuned LLMs, RAG provides improved quality of responses in specialized domains by leveraging diverse external knowledge [27]. Using effective prompts to guide LLMs [32], RAG chatbots can offer greater personalization and relevance compared to rule-based and hybrid systems [27].

However, the development of chatbots based on LLM and RAG techniques is still nascent in the context of HIV and PrEP support and has not been implemented to address implicit user needs [48], answer long contextual queries [48], and generate personalized responses [59]. Addressing these issues in RAG chatbots can lead to providing accurate, flexible, personalized, and human-like responses to promote PrEP uptake.

Available literature [60-62] and our prior work have identified human-like emotional support components as essential elements to support behavior change for HIV medication taking and PrEP uptake. However, no prior work has developed RAG chatbots to provide personalized information and peer experiential expertise (eg, user stories) [63]. Existing HIV and PrEP chatbots focus on providing informational support [19,22,29] and emotional support according to therapeutic guidelines [27]. Effective support provided by humans relies on emotional support (ie, human-like emotional support) [64,65] and peer experiential expertise (ie, peer experiential support) [63,66], elements which are absent in existing health care chatbots. Providing effective human-like emotional support first requires understanding and communicating complex human emotional experiences [67-69]. The Willcox Feeling Wheel facilitates this process by providing a related vocabulary for 6 emotion categories [69]. To better reflect the real-world user queries that often seek practical user experiences (peer experiential expertise) [70], PrEP chatbots should provide peer experiential expertise support through pragmatic narratives of others in similar situations, which might enhance self-coping skills and emotional confidence, leading to improved quality of life [63,66]. Additionally, chatbots need to support back-and-forth dialogue exchanges, similar to real-world help-seeking behaviors, to facilitate clarification and understanding of complex concerns [71].

We develop a RAG chatbot that addresses the knowledge gaps in providing personalized PrEP support. We develop a support classifier to identify implicit PrEP user needs from long context queries by fine-tuning Google’s Gemma model. The RAG chatbot uses personalized prompts and an external knowledge base (facts and real-world PrEP experiences) to generate responses tailored to users’ informational, experiential, and emotional needs. To provide human-like emotional support, we use the Willcox Feeling Wheel to guide the chatbot in responding empathetically to the emotions expressed by the user. The chatbot also provides personalized peer experiential expertise support through pragmatic narratives of users in similar situations. We use extensive prompt engineering and fact validation techniques to improve accuracy, personalization, and transparency in generated responses.

This protocol describes the development of a RAG chatbot in 2 phases. Phase 1 describes conceptualizing the chatbot architecture, and Phase 2 includes iterative development of the RAG chatbot to provide personalized information, peer experiential expertise, and human-like emotional support. Before prototype development, we conducted a separate study analyzing PrEP-related online social support exchanges to identify the social needs of PrEP candidates to conceptualize the chatbot architecture. Results of this study will be reported in a separate paper. We demonstrate the iterative development process focusing on prompt engineering, which is an integral part of the LLM RAG chatbot implementation. An exploratory user study was conducted to assess the quality of chatbot responses compared to real-world user responses and general LLM responses. We also discuss the implications of these findings, highlighting the feasibility of the chatbot prototype as a tool to facilitate future PrEP support.

To improve accuracy and empathy, we designed an RAG architecture that explicitly separates informational, experiential expertise, and emotional support. Unlike a traditional LLM, which processes a user query based solely on its internal training data, this RAG framework uses a support classifier to identify specific user needs. The RAG chatbot then queries specialized knowledge bases to deliver tailored, evidence-based responses to the identified social support needs. This modular approach helps in controlling hallucination and improving the accuracy of the support provided. The mechanism involves segmenting user queries into specific needs and retrieving information from separate factual and experiential databases; the system ensures that responses are both evidence-based and empathetically aligned. The key features of the RAG chatbot include the following:

To inform the goals of our RAG chatbot prototype, we analyzed 3020 publicly available Reddit conversations focused on PrEP from April 2011 to March 2024. We focused on the informational (including peer experiential expertise) and emotional (ie, psychosocial) support because these are the 2 main types of support that are exchanged in this forum and because these have the potential to impact broader behavioral trends related to PrEP use [72]. Our database for RAG aligns with the previous literature [73]. We found that more than 88% (2658/3020) of user PrEP needs included informational and emotional need items. Informational needs centered on PrEP facts and related experiences, and emotional needs focused on reassurance, HIV and PrEP concerns sharing, empathy, and sympathy, based on the psychosocial constructs of Social Support Behavior Code (SSBC) [74,75]. PrEP candidates’ emotions can be described with the Willcox Feeling Wheel [69], including sad, mad, scared, joyful, peaceful, and powerful.

Real-world user queries were multifaceted, consisting of multiple informational and emotional needs. Chatbots must (1) preprocess each input query into semantic segments, (2) generate responses for each segment, and (3) combine them into a final response. Based on literature [73] and our prior work, our chatbot was designed to (1) generate personalized responses, (2) provide accurate and comprehensive information to user queries, (3) provide peer experiential expertise support, and (4) respond empathetically to the emotions expressed by the user. Functional dialogue flow diagrams were created (Figure 1) to visually communicate with our team members and developers how the chatbot would achieve these aims.

To identify user needs, we created a PrEP-related dataset. This dataset was annotated with 3 categories: informational (including facts and peer experiential expertise), emotional, and context. Subsequently, we built a classifier to parse user inputs and categorize them into the 3 categories. The classifier further classified informational needs into facts and peer experiential expertise. For informational needs, we specifically designed the chatbot to provide information (eg, facts and actionable advice) and peer experiential expertise support (ie, other people’s experiences and opinions). For emotional needs, we designed the chatbot to identify user emotions based on the Willcox Feeling Wheel [69] and to generate a suitable response using relevant SSBC psychosocial constructs known to impact PrEP uptake (eg, reassurance, understanding, empathy, sympathy, validation, relief of blame, and compliment) [60,76].

The RAG module consists of an information retriever and an LLM generator.

Once all the necessary data were retrieved, we instructed the LLM (Gemini-2.0-flash) to generate responses using the retrieved information. This process was guided by personalized prompts (PI [personalized information], PE [personalized emotion], and PX [personalized experience]) and the user need (ie, informational, peer experiential expertise, and emotional responses) as described in Table S1 in Multimedia Appendix 1. We iteratively refined the prompts via prompt engineering to improve comprehensiveness, accuracy, relevancy, transparency, and response coherence. The PI-2 prompt instructs the LLM to provide a detailed response, followed by the PI-3 prompt to check the consistency of factual queries. For peer experiential expertise queries, we used the PX prompt that instructs the LLM to use the retrieved documents and generate a third-person narrative with explicit statements of partner conversations and direct quotes, when possible, to emphasize transparency.

For providing human-like emotional support, we used PE-1 to first identify the feelings based on the 6 Willcox feeling categories and to generate a suitable response reflecting SSBC psychosocial constructs (ie, sympathy, understanding, empathy, relief of blame, validation, compliment, or reassurance) for the identified feeling. Identifying user feelings allows responding with suitable emotional expressions that are typically associated with human responses. We also found that general LLM responses to emotional queries lacked deeper understanding of underlying emotions and human-like emotional behaviors, consistent with findings in previous literature [90]. The RAG LLM was instructed to use the style and tone from the retrieved emotional documents, which comprise peer-to-peer emotional support. When no relevant documents were retrieved for an emotional query, we used the PE-2 to generate a suitable response reflecting SSBC psychosocial constructs for the identified user feeling, without using the style and tone from the retrieved documents. Fact responses were validated by the fact validator. Then, the cohesive response (CR) prompt combines all subquery responses, instructing the LLM to smooth the information flow into a single CR. Finally, the LLM provides a combined response.

Responses providing factual information were validated with the documents from the facts database, a step toward eliminating LLM hallucination. Our fact validation algorithm consists of 2 steps: fact identification and fact validation. To reduce overreliance on LLMs, we used a deterministic technique to identify semantically relevant facts from the retrieved documents (ie, the same top 5 documents retrieved from the information retriever process). This is done to address a known issue: that LLMs often provide imperfect fact identification when they break down complex information [91,92]. To identify the most relevant facts for validation, we (1) segmented the generated response and retrieved documents using the SAT tool and then (2) extracted the most relevant fact-supporting document segment (step 1) based on SBERT and cosine similarity (ie, highest similarity score). This creates pairs of generated response segments that correspond to their fact-supporting document segments. The LLM was instructed to use the response-document pairs and the FV-1 prompt (step 2) to identify any response segments that are not supported by the document segments identified with cosine similarity. Providing the subset of relevant fact segments to the LLM narrows its focus, improving validation accuracy. Then, using any generated response segments with inaccurate facts and the FV-2 prompt, the LLM was instructed to extract supporting facts from the retrieved documents (step 3).

To evaluate the performance of the fact validator, we manually verified the performance for 1033 fact response segments from 100 generated responses for randomly sampled query responses that the LLM generated. The fact validator achieved a 97.39% validation accuracy based on 1006 sample items. The accuracy was calculated as the percentage of fact response segments that were fully supported by the facts database, as determined by the research team. Only 3% (27/1033) of the generated response segments were not able to be validated with the facts database (95% CI 1.85‐3.88). These included response segments providing general information (n=12, eg, “Other side effects [of the medication] include changes in the immune system, dizziness, diarrhea, nausea, vomiting, headache, rash, and gas.”) and personalized information (n=8, eg, “Given the recent unprotected anal sex with someone who recently learned they had syphilis, your friend should get tested for STIs as soon as possible.”). We also found false negatives (n=7), where the LLM extracted facts that could not support the generated response segment in step 3. For example, when validating a response segment (eg, “Truvada alone is not sufficient for post-exposure prophylaxis (PEP).”) with an accurate and semantically retrieved document segment, the LLM extracted facts that could support the generated response segment (eg, “It is worth noting that Truvada must be used correctly (without skipping doses) whether it is used in combination with other ARVs in the control of HIV, or for PrEP.”). Based on these data, we opted for a 3-step validation process because the semantic retrieval (step 1) identified over 82.10% (848/1033) of accurate response segments, whereas the LLM identified (step 2 and step 3) 15.30% (158/1033) of accurate response segments. It is also important to note that we also found that LLM-based validation was less consistent compared to the deterministic techniques. For instance, when we processed the same sets of 100 samples 10 times, the LLM changed its final validation responses (step 2 and step 3) in 44% of samples. It is important to note that the 44% does not indicate that earlier responses were inaccurate. Rather, the nondeterministic nature of LLMs (as probabilistic models) relying solely on LLMs can hinder the validation process. This highlights the need for mixed validation approaches, combining deterministic techniques and LLMs to eliminate LLM hallucinations.

Two types of questionnaires were developed for the study using Google Forms. The demographic questionnaire collected participant information on demographic and professional experience. The questionnaire consists of 6 deidentified questions asking participants about their age, gender, ethnicity, race, region, and professional background.

The chatbot response evaluation questionnaire was used to assess the quality of the responses based on 10 criteria (Table 1): information accuracy [89,94,96], information clarity [94,95], information actionability [96], information detail [95], relevance [89,94], tailored information [89,94], comprehensiveness-question answered fully [95], suitable tone [95], empathy [89], and suitable language [94]. The questionnaire contained instructions and definitions of each criterion along with the set of PrEP queries and corresponding responses. Participants were required to rate each response on a 5-point Likert scale for all criteria. Based on prior literature on expert-reviewed chatbot responses [99] suggesting good to excellent performance, we determined a response quality rating of above 4 on a 5-point scale for each of the 10 evaluation criteria.

To account for the nonindependence of observations where multiple ratings were provided by the same participants, a linear mixed-effects model was used using the lme4 package in R (v4.3.1; R Core Team). With 7 participants evaluating 15 queries across 3 response types and 10 evaluation criteria, the model analyzed a total of 3150 response evaluations. Participants and queries were treated as random effects to control individual rater bias and item variability. Quantitative analysis was conducted using R (R Core Team) and Microsoft Excel.

We have developed a RAG chatbot prototype as of January 2025 and have iteratively refined the chatbot design and system based on feedback from internal evaluation.

To assess the reliability of the RAG chatbot in identifying support needs, we validated the 3-class support classifier in categorizing queries into informational, emotional, and contextual needs.

As reported in Tables 2 and 3, our fine-tuned Gemma 2 2B-it model demonstrated strong performance in classifying PrEP support needs when fine-tuned on the full dataset (F₁-score=0.86) and class-balanced subset (F₁-score=0.89). Specifically, we found that fine-tuning the Gemma 2 2B-it model on a class-balanced subset indicated strong accuracy and precision in classifying emotional needs, while maintaining comparable performance in classifying informational and contextual queries (Table 2). Based on superior performance indicated by F₁-score metrics, we used the class-balanced Gemma 2 2B-it model during preprocessing for classifying user support needs. In the context of classifying informational and emotional support seeking, our class-unbalanced BERT model exhibits superior performance (F₁-information score=0.92, F₁-emotion score=0.71) compared to a previous social support seeking dataset, CHQ-SocioEmo (F₁-information score=0.58, F₁-emotion score=0.62). This classification of user queries allows our chatbot to identify user needs, extract context, and generate tailored and emotionally relevant responses.

We manually observed that prompt engineering plays a significant role in harnessing LLM capabilities to generate desired outputs. We report key findings from our iterative prompt engineering, offering suggestions for effective prompting in chatbot applications. Although there is no standard approach for refining prompts [103], starting with simple and direct instructions (Table S2 in Multimedia Appendix 1) helped in understanding LLM comprehension and reasoning ability. For complex and longer queries, we found that prompting the LLM to decompose the query into segments helps in improving the response comprehensiveness (eg, PI-2 prompt from Table 1). Such prompt decomposition was also effective in responding to subtle user emotions by first identifying the emotions from a list of emotion categories and guiding the LLM in social-emotional reciprocation for personalizing emotional responses (ie, PE-1 and PE-2 prompts).

An important finding in LLM prompt engineering was the limitation of using multiple instructions in a single prompt. We found that LLMs indicate low prompt adherence when a prompt has multiple instructions. In such cases, the LLM often defaults to the last instruction, resulting in partial and inconsistent execution of complex logical reasoning prompts. Another challenge was the LLM’s sensitivity to linguistic variability in prompts [104]. We found that paraphrasing prompts by modifying prompt structure, using synonyms, and expanding prompt context is ineffective in context management tasks (eg, identifying the need for additional context), exhibiting inconsistent response output. We found that shorter prompts with content words summarizing the main concept or idea can reduce ambiguity in LLM decisions.

A few-shot prompting approach is effective in text classification [49]. We found that few-shot prompting did not consistently perform well across classification for informational needs, emotional needs, and context. This limitation is potentially because these categories represent high-level support needs that require diverse perspectives for inferring ambiguous context and broad scope. Consequently, fine-tuning LLMs on annotated datasets produced more reliable results than few-shot prompting. Few-shot prompting is effective in simple subclassification tasks with clear and distinct criteria for narrow scope [105].

For fact validation, deterministic techniques, including semantic relevance and text similarity metrics (eg, ROUGE and BLEU), often failed due to their limited ability to capture negations [106] and numerical accuracy [107]. We found that LLMs generally struggle to validate statements that are generalized or personalized and are not directly present in the knowledge base. When required facts are scattered across documents, such generalized or personalized statements need implicit validation and cross-referencing for accurate verification of information [108].

The exploratory user study included 7 participants who completed the full evaluation protocol; the majority of participants were female (5/7, 71.4%) and aged 25‐34 years (5/7, 71.4%), with an Asian (5/7, 71.4%) ethnic background. Across 15 real-world queries evaluated against 10 quality criteria, the study generated 3144 valid observations, with 6 “NA” ratings (instances where a criterion was deemed nonapplicable by the rater).

The overall performance of the 3 response sets (general LLM, RAG chatbot, and user) was evaluated across 10 distinct evaluation criteria. Due to nonindependence of data points, a linear mixed model was used to account for user bias and random effects from both participants and specific questions.

Initial observations indicate variations in response quality across the 3 groups, suggesting specific areas for improvement in the RAG chatbot architecture and prompt design based on the evaluation criteria. Both AI modalities (general LLM and RAG) had higher preference across the evaluation criteria compared to user responses. We observed that the general LLM’s communication style appeared to influence how participants rated comprehensiveness, detail, and tailoredness of the information received, though both AI models showed similar performance in terms of empathy and tone.

Given the exploratory nature of this pilot evaluation, the sample size (n=7) was small and skewed in terms of participant demographics (eg, primarily consisting of university students), which limits the generalizability of these findings to the broader PrEP-candidate population. However, these exploratory insights into future modifications demonstrate the viability of the evaluation framework and provide a descriptive baseline for the heuristic and user study outlined in this protocol.

We describe the development of a RAG chatbot prototype for providing personalized support to fulfill informational, peer experiential expertise, and emotional needs of PrEP candidates to facilitate PrEP care. The chatbot is designed for (1) identifying support needs and personalizing responses (tailored information, clarity, and tone); (2) providing complete, relevant, and accurate HIV and PrEP information (comprehensiveness, detail, relevancy, accuracy, and language); (3) providing peer experiential expertise support (relevancy and actionability); and (4) responding with human-like emotional support (empathy).

Our exploratory user study results provide initial empirical support for the proposed chatbot functionality. Both the RAG chatbot and the general LLM were preferred over user responses across all 10 evaluation criteria. This suggests that AI-generated support can offer an enhanced level of perceived utility, informational depth, and structural coherence compared to traditional peer support related to PrEP.

The analysis revealed a performance gap between the two AI architectures. The general LLM performed better than the RAG chatbot in 8 of 10 categories, with the exceptions of “empathy” and “tone.” While RAG techniques are intended to reduce hallucinations [48], improve content diversity [58], user privacy [53], and personalization [54,55], our results indicate that the logical reconstruction of fragmented information remains a challenge for RAG-based systems [108].

LLMs can maintain superior structural fluidity and generate well-organized information [109]. Our results indicated higher ratings for the general LLM in stylistic criteria, such as structure and communication style. In contrast, we observed that the RAG system’s output was inherently tied to multisource retrieval and the informal nature of the retrieved peer-discussion data. However, the practical advantage of our RAG approach lies in its modular architecture, which is specifically designed to facilitate control over response accuracy and prevent the generation of fictitious experiences by relying on verifiable information. Furthermore, our RAG chatbot integrates anonymized real-world peer experiential expertise and Willcox emotional vocabulary to provide a level of cultural authenticity and empathetic alignment that a general-purpose model cannot guarantee. Another advantage of RAG models is their ability to use smaller, localized language models to achieve response quality comparable to significantly larger LLMs [110,111], thereby providing opportunities to reduce health care barriers related to data privacy and operational costs.

The participant group expressed a preference for the structured and categorized communication style of the general LLM over the RAG chatbot’s response format. This suggests that while RAG can be highly effective in extracting information from diverse sources to provide contextually intelligent responses [112], further refinement is needed to synthesize retrieved fragments into a clearer and more structured response format that matches the systematic response delivery of general-purpose LLMs.

Our analysis revealed a residual variance of 0.662 within the linear mixed model, suggesting random noise within the rater data that warrants further investigation. We believe that this inconsistency is primarily attributable to user fatigue stemming from the study’s cognitive load. Each participant was required to evaluate 15 complex query sets across 10 distinct criteria, resulting in 450 total data points per rater.

Furthermore, the high level of domain-specific expertise required to evaluate PrEP support, specifically the distinction between clinical accuracy and nuanced peer experiential support, may have been impacted by the nonexpert and nontarget user status of the participant group. As the raters were neither clinicians nor PrEP candidates, their evaluations of criteria such as “accuracy,” “actionability,” “clarity,” “comprehensiveness,” “detail,” “empathetic,” “language,” “relevance,” “tailored information,” and “tone” represent perceived credibility rather than objective medical validation. For criteria related to information quality (accuracy, clarity, actionability, and detail), raters lacked the clinical training required to verify the medical correctness of PrEP information and practical guidance. Similarly, evaluating criteria on response relevance and appropriateness (relevance, tailored information, and comprehensiveness) requires an understanding of PrEP challenges such as medical mistrust, PrEP navigation, or side-effect management, commonly experienced by the target population. Furthermore, HIV experts and PrEP candidates are more familiar with PrEP terminology and community lingo, which may impact the perception of tailored information, language, and trust, as these target users can distinguish between generic AI responses and grounded, community-informed experiential support.

The low incidence of “N/A” ratings (n=6) suggests that the questionnaire structure may have lacked sufficient clarity in guiding participants to opt out when specific criteria were not relevant to a particular question sample. Improving the questionnaire structure to better highlight the specific intent of each rating and providing more robust instructions will be critical to ensure rater reliability. These exploratory observations will directly inform the next phases of our development, as detailed in the “Future Direction” section.

Although recent advances in hallucination removal have improved LLM reliability, these advances do not entirely eliminate the need for validating response accuracy, especially in health care applications [92]. Due to linguistic complexity and nuances in generated responses, LLMs struggle to consistently and accurately validate factual information using only RAGs [119]. Additional methodological improvements are needed to validate broad facts outside of the database and context-dependent information.

Our system faces several inherent risks and ethical considerations. First, the complexity of human emotion makes personalized digital support a challenging endeavor, with risks of stigma reinforcement or user overreliance for clinical advice. Second, while using community-sourced data (eg, Reddit) is practical, it may introduce demographic or topical biases that limit the system’s generalizability across diverse PrEP user populations. We intend to extend the datasets from MedHelp and POZ communities to mitigate these gaps.

A limitation of this study is the absence of a comprehensive quantitative analysis comparing the performance of few-shot prompting and fine-tuning for support classification. While our study provides initial qualitative insights into the effectiveness of these 2 approaches for specific tasks, these findings are exploratory. Future work should include extensive empirical comparisons to statistically generalize these results across broader datasets.

Another limitation of the current evaluation is that the user study was conducted with people who were nontarget PrEP-using demographics and nonclinical experts. While this provides insight into general user perception and readability, it may not capture the technical accuracy or perceived personalization of the responses with precision. Future work will include larger-scale iterative evaluations to identify real-world failure scenarios and ensure the model’s safety profile. Furthermore, subsequent iterations will focus on demographic-aware personalization to better reflect the diverse cultural preferences, behaviors, and potential linguistic needs of the PrEP users, addressing the wider ethical implications of using sensitive, public-domain social media data for health support.

The current chatbot model was developed and accessed in a research setting and will be rigorously tested for safety and reliability with experts and the target population prior to real-world deployment. Building on our exploratory findings, we will conduct a larger-scale user study for gathering feedback on chatbot responses from the target population. We will also concurrently initiate a heuristic evaluation with HIV prevention experts to validate clinical safety, user trust, ethical alignment, and factual accuracy of the PrEP chatbot.

To bridge the gap between initial research product and sustainable real-world health intervention, the chatbot design will incorporate sociotechnical design considerations [120] through the Accelerated Creation-to-Sustainment (ACS) framework [121]. Through these community-representative user and expert evaluations, we will gather feedback on cultural and behavioral preferences to improve long-term adoption and user trust. This includes incorporating multilingual support to cater to linguistic needs. Furthermore, we will evaluate technological flexibility, including low-cost, lightweight local deployment models and multiplatform accessibility (eg, mobile and web-based interfaces), to lower entry barriers in resource-limited settings. To ensure long-term sustainability, we plan to engage with HIV health stakeholders to establish frameworks for managing scalability and routine content updates, ensuring the intervention remains clinically accurate and relevant as the HIV prevention landscape evolves.

This study details the implementation of a RAG chatbot prototype designed to provide personalized informational, peer experiential expertise, and emotional support to PrEP candidates. By offering personalized information and peer experience, our chatbot can provide essential information for making informed decisions about health. In addition, our chatbot’s capability to provide human-like emotional support highlights its potential to address stigma related to PrEP. Although current observations are preliminary, we believe our chatbot has the potential to clarify misconceptions and provide PrEP support through these mechanisms. Future evaluation using expert and real-world feedback will validate and improve the chatbot’s capability; after this, a trial to demonstrate the efficacy of the tool to increase PrEP uptake and retention will be proposed.