What is Context Engineering

How It Works

Context engineering operates at three layers. The ingestion layer determines what data enters the system and how it is decomposed -- video into frames, keyframes, and transcripts; documents into sections and tables; audio into segments and speaker turns. The indexing layer determines how that data is represented -- dense embeddings, sparse keywords, structured metadata, taxonomy labels -- and where it is stored for retrieval. The assembly layer determines how retrieved context is selected, ranked, compressed, and formatted for the model's context window. Each layer involves design decisions that compound: poor chunking at ingestion means no retrieval strategy can compensate.

Context Engineering vs. Prompt Engineering

• Prompt engineering optimizes a single query; context engineering optimizes the entire information supply chain
• Prompt engineering is a runtime activity; context engineering is mostly a design-time and build-time activity
• Prompt engineering assumes the right information is already available; context engineering ensures it is
• A good prompt cannot compensate for missing or poorly structured context
• Context engineering encompasses RAG pipeline design, embedding model selection, chunking strategy, retrieval tuning, and tool configuration

Why It Matters Now

Three trends make context engineering critical in 2026. First, context windows have expanded (200K+ tokens) but filling them naively degrades performance -- careful context selection outperforms brute-force inclusion. Second, AI agents with tool use need structured, retrievable context rather than monolithic prompts. Third, enterprises are moving from text-only RAG to multimodal RAG, which requires context engineering across video, images, audio, and documents simultaneously. The organizations that treat context as an engineering discipline rather than an afterthought are seeing dramatically better AI outcomes.

Key Components

• Feature extraction: decomposing raw files into searchable, structured features (embeddings, metadata, taxonomy labels)
• Retrieval design: choosing between dense, sparse, and hybrid retrieval strategies for different query types
• Context assembly: selecting, ranking, and formatting retrieved results to maximize relevance within token limits
• Tool design: exposing structured capabilities (search, classify, extract) that agents can invoke to build context on demand
• Memory management: maintaining conversational and long-term context across agent interactions

Best Practices

• Start with the task and work backward to the context it requires, rather than starting with available data
• Invest in feature extraction quality at ingestion time -- richer features enable better retrieval later
• Use multiple retrieval strategies (semantic search, keyword matching, metadata filters) and let the query determine the mix
• Measure context quality with retrieval metrics (recall@k, MRR) not just end-to-end generation quality
• Treat context pipelines as software: version them, test them, monitor them in production