# What is Context Engineering? The Complete Guide (2026)

Last updated: April 2026 | Reading time: 8 min

> "The hottest new skill in AI is not prompt engineering — it's context engineering."
> — Andrej Karpathy

If you've been building with LLMs, you've noticed something: the model is rarely the bottleneck. The context is. Two developers using the same model get wildly different results — the difference is what information they put in the context window.

Context engineering is the discipline of automatically selecting, compressing, and delivering the right information to AI models at the right time.

## The Context Window is RAM

Think of an LLM like a CPU. The context window is its RAM. Everything the model knows about your task must fit in that window — your code, your question, your docs, your conversation history.

The problem: RAM is finite and expensive.

- GPT-4 Turbo: 128K tokens (~300 pages)
- Claude Opus: 200K tokens (~500 pages)
- A medium codebase: 500K-2M tokens

Your codebase doesn't fit. So every AI tool makes a choice: which code to include and which to ignore. Most tools make this choice poorly — they stuff files until the window is full, then cut. The result is an AI that sees 5% of your code and hallucinates the rest.

## The Three Layers of Context Engineering

### Layer 1: Selection — What Goes In

Which code fragments, documents, and context should be included? This is where most approaches fail — they use embedding similarity (RAG) which misses dependencies, ignores information density, and can't guarantee optimality.

An ideal selection system:

- Considers all fragments, not just top-K similar ones
- Accounts for dependencies — if you include auth.py, include auth_config.py
- Ensures diversity — don't send 3 auth files and 0 database files
- Is mathematically optimal — proven best subset for the budget, not approximate

### Layer 2: Compression — How It Fits

Not all code needs full resolution. A file your AI needs to edit should appear in full. A utility it might call only needs the function signature. A config file it should know exists only needs a one-line reference.

Variable resolution is the key insight: represent your entire codebase at different levels of detail, allocating more resolution to more important code.

### Layer 3: Learning — Getting Better Over Time

Which context produced good AI responses? Which selections led to hallucinations? A context engineering system should learn from every interaction and improve its selection weights over time.

This is where reinforcement learning enters: the system observes outcomes and adjusts, without manual tuning.

## Context Engineering vs Prompt Engineering

| Prompt Engineering | Context Engineering
--- | --- | ---
Focus | How you ask | What information the AI sees
Scale | Manual per-prompt | Automated per-request
Handles code | You paste it manually | Auto-selects optimal subset
Learns | You iterate manually | RL optimizes automatically
Token efficiency | Low (you guess) | High (mathematically optimal)

Prompt engineering is about how you phrase the question. Context engineering is about what information the AI has access to when answering. Both matter. But context engineering has far more leverage — the best prompt in the world can't fix an AI that can't see your code.

## Context Engineering vs RAG

RAG (Retrieval Augmented Generation) is one approach to context selection. It uses embedding vectors to find chunks similar to your query. But RAG has fundamental limitations for code:

| RAG | Context Engineering
--- | --- | ---
Selection method | Cosine similarity | Information-theoretic optimization
Coverage | Top-K similar chunks | Supported files at variable resolution
Dependencies | Missed | Automatically included
Duplicates | Sent multiple times | Detected and merged
Needs embeddings | Yes (API cost + latency) | No (runs locally)
Optimality | Approximate | Knapsack-optimal (proven for the scoring objective)

RAG answers the question "what's similar to my query?" Context engineering answers the question "what's the optimal information set for this token budget?"

## The Context Engineering Stack

A complete context engineering system has these components:

1. Indexer — Maps your codebase: files, dependencies, symbols, types
2. Scorer — Ranks fragments by information density (Shannon entropy, not embedding similarity)
3. Deduplicator — Finds and merges duplicate/near-duplicate code (SimHash, LSH)
4. Selector — Picks the optimal subset for a token budget (knapsack solver, submodular optimization)
5. Compressor — Represents code at variable resolution (full, skeleton, reference)
6. Learner — Tracks outcomes, updates selection weights (reinforcement learning)

## Entroly: Context Engineering, Automated

Entroly is an open-source context engineering engine that implements all six components. It integrates with compatible local tools such as Cursor, Claude Code, Windsurf, and Cline, then delivers selected context for each request.

Key results:

- 78% fewer tokens per request (measured across real codebases)
- Variable-resolution coverage — supported files represented at some resolution
- <10ms overhead — Rust engine, imperceptible latency
- Learns from usage — REINFORCE-based weight optimization
- Zero config — one command setup

    pip install entroly[full] && entroly go

## The Future of Context Engineering

As context windows grow (1M+ tokens), you might think context engineering becomes less important. The opposite is true:

- Larger windows = more wasted tokens — filling 1M tokens with raw code is even more expensive
- Cost scales linearly — 10x more tokens = 10x more cost
- Attention degrades with length — models perform worse with more irrelevant context ("lost in the middle" problem)
- Multi-agent systems — multiple AI agents sharing a token budget need allocation, not just stuffing

Context engineering isn't a stopgap for small context windows. It's a permanent discipline — like database query optimization didn't disappear when RAM got cheaper.

### Automate your context engineering.

Entroly selects context for your AI automatically. Open source. Zero config.

    pip install entroly[full] && entroly go

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