Context Management: The Foundation of Modern LLM Applications

Large Language Models (LLMs) have transformed how we build software. But their power comes with a fundamental challenge: they’re only as good as the context they receive. In this article, we’ll explore how proper context management forms the foundation of modern LLM applications, and how knify is implementing advanced solutions to this challenge.

The Context Challenge in LLM Applications

When we work with LLMs in web development, the quality of responses depends heavily on the context we provide. Too little context, and the LLM can’t generate accurate or relevant responses. Too much irrelevant context, and we waste tokens, increase costs, and potentially dilute the quality of the response.

This challenge becomes particularly acute in complex applications like:

• Code assistants that need to understand your project structure
• Customer support bots that need user history and product documentation
• Document analysis tools that need to combine multiple files

knify’s Hybrid Approach to Context Management

At knify, we’re building a highly opinionated framework that tackles this challenge through a hybrid context management system combining rule-based and dynamic approaches:

Rule-Based Context Selection

This uses predefined rules or patterns to include certain context. For example:

• When a user’s query mentions a specific function, we automatically include that function’s source code
• When an error message is detected, we include relevant documentation or previous solutions
• For domain-specific queries, we inject specialized knowledge from our documentation

This approach ensures predictable and transparent context inclusion, covering known scenarios with precision.

Automated Dynamic Context Selection

To complement the rules, we implement retrieval-augmented generation (RAG) that searches a knowledge base for content similar to the query:

• We maintain embedding vectors of code repositories, documentation, and user conversations
• Our similarity search fetches functions or docs related to the user’s question
• This handles open-ended queries and unforeseen contexts by discovering relevant information automatically

The Power of Integration

By combining both approaches, our system leverages the strengths of each:

• Rules provide high-precision targeting for known scenarios
• Dynamic retrieval offers broad recall for unexpected questions
• Together, they create a comprehensive context that significantly improves relevance and reduces hallucinations

Developer-Friendly Design

Context management shouldn’t create additional complexity for developers. Our approach focuses on being:

• Configurable but automatic: Simple configuration with sensible defaults
• Middleware architecture: Our context manager sits between the user’s request and the LLM
• Pluggable sources: Easily connect to codebases, documentation, wikis, and more

# Example of using the context manager in knify
cm = ContextManager(rules=default_rules, sources=project_files_index)
full_prompt = cm.prepare_prompt(user_query, conversation_history)
response = llm_api.call(full_prompt)

Real-World Impact

The difference between basic prompting and advanced context management is dramatic:

• Better code completions: When provided with relevant repository context, LLMs can maintain consistent coding patterns and follow project conventions
• Reduced hallucinations: Grounding the model with retrieved facts significantly reduces the chance of generating incorrect information
• Lower costs: By intelligently selecting only the most relevant context, we reduce token usage and API costs

Looking Ahead: The Future of Context in knify

Context management is just the beginning. As we continue to develop knify, we’re exploring:

• Adaptive context selection that learns from user feedback
• Hierarchical context structures for nested information relevance
• Compression techniques to include more context within token limits

In our next article, we’ll explore how modern testing approaches for LLM applications are changing the development landscape and how knify is incorporating these innovations.

Stay tuned as we continue to explore how LLMs are reshaping modern web development!