The secret to a truly helpful product AI isn't just a better LLM—it's a superior context strategy. Discover how to transform your AI from a generic chatbot into an indispensable, expert assistant.
Providing relevant, real-time information drastically improves AI performance, turning frustrating interactions into valuable outcomes.
+40%
Accuracy Boost
Grounded responses based on factual, up-to-date information.
-75%
Hallucinations
Reduces instances of incorrect or fabricated information.
+60%
User Trust
Reliable answers build confidence and encourage adoption.
Context engineering is primarily powered by RAG. This process finds relevant information from your private data sources and provides it to the LLM at the time of a query, ensuring responses are timely and accurate.
User Query
Retrieve Relevant Context
(from Docs, DBs, APIs)Assemble Prompt
(Context + Query + Instructions)LLM Generation
Grounded AI Response
Building a robust context strategy relies on several key best practices. Mastering these pillars is essential for creating a reliable and intelligent product AI agent.
A successful agent doesn't rely on a single source of truth. It integrates various layers of information to form a complete understanding. This chart shows a balanced mix for a typical product AI.
Finding the right context is crucial. While vector (semantic) search is powerful for understanding intent, combining it with keyword (lexical) search handles specific terms and codes far more effectively, boosting overall accuracy.
How you format the context for the LLM matters. Clearly separating context from instructions using tags (e.g., <context>) helps the model focus on the right data, significantly reducing errors.
-25%
Factual Errors
when using structured data formats in prompts
Context engineering is not static. By monitoring agent performance, analyzing failures, and continuously refining the knowledge base and retrieval strategies, you can achieve consistent improvements in accuracy over time.