Beyond Basic Prompts: Solving the User Context Puzzle in LLM App Development

Building truly intelligent applications with Large Language Models (LLMs) often hinges on one critical, yet cumbersome, factor: user context. Without understanding who the user is, their journey within the app, or their past interactions, LLMs operate in a vacuum, leading to generic or even unhelpful responses. A recent Hacker News discussion, initiated by a developer grappling with this very issue, sheds light on the common pain points and explores potential solutions for seamlessly integrating rich user context into LLM prompts.

The Developer's Dilemma: The Pain of Manual Context Pipelines

The original poster (OP), marcospassos, described a common frustration: repeatedly building manual pipelines to feed user context to LLMs. This involves tracking user events, enriching session data (e.g., with geolocation from IP, device details), summarizing behavior, and then injecting this synthesized context into prompts. While this makes the LLM's responses significantly more helpful, the engineering effort is substantial and repetitive.

Examples of how this context is used include:

• Support: Passing information about documents viewed or error pages encountered.
• Marketing: Summarizing the user's journey (e.g., 'ad clicked' → 'blog post read' → 'pricing page').
• Sales: Highlighting behaviors indicative of user type (e.g., startup vs. enterprise).
• Product: Classifying session intent (e.g., 'confused', 'exploring plans', 'ready to buy').
• Recommendations: Generating embeddings from recent activity to improve content matching.

The OP wished for a simple getContext() function that would provide a rich, prompt-ready summary, such as: "First-time visitor using Google Chrome on a MacBook, browsing from San Francisco. Landed on the pricing page from a Google ad, clicked to compare plans, then visited the enterprise section before initiating a support chat."

The Vision: An Automated "Segment for LLMs"

The core idea proposed is a service that handles the heavy lifting of context gathering end-to-end: tracking user interactions (like a Hotjar or analytics script), interpreting this data, enriching it, and outputting a concise, LLM-friendly summary. This would be akin to what Segment does for customer data, but specifically tailored for providing real-time behavioral context to LLMs.

Community Perspectives and Current Strategies

Commenters shared various approaches to managing user context:

• Custom Prompt Engineering and State Management: User coolKid721 emphasized custom-tailoring prompts with only pertinent context and recommended viewing LLM requests as "singular atomic interactions." They use Elixir Phoenix with genservers to maintain user state and dynamically generate prompts, also pointing to Anthropic's prompting documentation as a valuable resource.

• Leveraging Embeddings and Vector Databases: ProfessorZoom described a system where disparate pieces of information are embedded and stored as vectors in a database. The user's query is also embedded, and a similarity search retrieves the most relevant contextual information to be included in the prompt.

• Traditional State Management: matt_s likened LLM API calls to regular function calls, where inputs (including context) need to be tuned. For persistence between sessions, they suggested standard database solutions (like SQLite for non-web contexts) or even simple files.

• Utilizing Existing Analytics (with a Twist): max_on_hn suggested querying analytics tools like Mixpanel or PostHog at runtime for raw data, then using a generic summarizer. However, the OP noted that the goal was to avoid this manual summarization step.

• Incremental Session Summaries: rcarmo compared the desired context to what's used for breadcrumbs, suggesting maintaining a running summary within the user session and re-packing or further summarizing it when it exceeds a certain threshold.

• Architectural Patterns: The MCP (Model-Context-Presenter or similar) pattern was mentioned by barbazoo and esafak as a potential way to structure context management, with esafak noting the "C in MCP is Context." However, OP marcospassos pointed out that while MCP might help with context generation, the primary effort lies in the tracking, processing, and enrichment pipeline.

One commenter, bilater, pointed to an existing service, context7.com, though the OP found it focused more on external data sources rather than the in-app user journey.

Is There a Product Here? Validation and Challenges

Several commenters, like nico, expressed strong interest in the OP's idea, seeing significant value in a "Hotjar/analytics script" that automatically collects context for prompts. nico even encouraged the OP to build it and share it on Hacker News.

However, potential challenges were also raised. esafak questioned what the "moat" or sustainable competitive advantage for such a "context inference service" would be, a point the OP acknowledged as something to figure out. The diversity of application-specific context needs, highlighted by enos_feedler, also suggests that a one-size-fits-all solution might be complex to build effectively.

Conclusion

The discussion underscores a clear need in the LLM application development space: a more streamlined, automated way to provide LLMs with rich, real-time user context. While developers are currently employing a variety of manual and semi-automated methods, the allure of an out-of-the-box solution is strong. Such a tool could dramatically reduce boilerplate, accelerate development, and ultimately lead to more intelligent, personalized, and effective AI-powered experiences.