Context engineering focuses on what information a reasoning model sees, not merely how you phrase the request. It treats the model as a reasoning core and surrounds it with accurately selected, position-aware and cost-aware evidence. Prompt engineering remains vital for tone and intent, but the difference is that context engineering treats retrieval, trimming, ordering and safety gates as first-class operations. Research on retrieval-augmented generation, long-context compression and reranking shows that output quality hinges on disciplined context supply more than on fancy wording.123
Context engineering
The practice starts from the model's context window—the byte budget the transformer can read at once. LangChain notes that overflow raises errors and that memory strategies are needed even for 100 K-token models.4 Pinecone experiments show recall falls when developers "stuff" too many documents into that window, because attention to middle tokens collapses.5 Hence context engineering asks three questions for every task: What must be present? Where should it sit? How many tokens can I spend?
Typical moves include:
- static inclusion of task-specific reference texts;
- on-the-fly retrieval with RAG or RETRO-style look-ups;
- lossless or lossy compression of long passages;
- position-aware ordering to avoid the "lost-in-the-middle" dip.
The RETRO paper from DeepMind showed that a 2-trillion-token retrieval store let a 7-B parameter model match GPT-3 on language modelling while using 25 × fewer weights, proving that smart context beats raw scale.6
Prompt engineering
OpenAI's help-centre guide treats prompt engineering as instruction design: clear role, formatting hints, examples and temperature control.7 Those rules optimise the decoder's behaviour once the context is fixed. Prompt tweaks seldom rescue a task if the model lacks the facts; context engineering supplies them.
Side-by-side comparison
Techniques and patterns
Retrieval-augmented generation
The 2024 survey on RAG catalogues a three-stage loop: encode, retrieve, generate.8 OpenAI's builder guide for GPTs frames RAG as "injecting external context at runtime" to improve accuracy.9 Effective pipelines use dual encoders for fast recall and cross-encoders for reranking, as Pinecone's tutorial illustrates.10
Context compression
Weaviate's context compression card defines a two-step routine: base retriever → compressor that trims redundant span.11 Research prototypes go further: ICAE compresses fourfold by writing memory slots that the same LLM can read.12 DAST allocates variable "soft tokens" to denser chunks instead of uniform quotas.13 LongLLMLingua prunes prompts to cut latency by 2 × while raising QA scores.14 QwenLong-CPRS adds multi-granularity compression guided by natural-language rules, hitting 21 × compression without quality loss.15
Dynamic retrieval vs static inclusion
Borgeaud et al. found that retrieval generalises well only when overlap between store and test text is controlled; otherwise performance gains are superficial.16 Blend static ground-truth documents (e.g., policy manuals) with retrieval for open-world queries.
Safety and instruction hierarchy
Anthropic's Constitutional AI shows that alignment instructions can be codified as an always-present preamble, ensuring safe reasoning regardless of user input.17 Context engineering therefore reserves headroom for policy blocks and verifies they remain in the first tokens.
Risk management
A 2025 risk-mitigation framework for RAG maps attack surfaces—from poisoning a vector index to exfiltrating private data—and recommends audit logs plus output scanners.18
Workflow blueprint
- Define evidence contract. List the documents or fields the model must see for any query; tag each with a freshness policy.
- Chunk and embed. Choose a splitter that respects semantic boundaries; produce embeddings compatible with your vector store.
- Retrieve with headroom. Fetch top-k greater than window capacity, then rerank and trim.
- Compress and order. Apply learned compressors or rule-based summarisers; place critical spans near the window edges that the model attends to most strongly.
- Attach system and user messages. Insert task framing, output schema and guardrails only after evidence is locked.
- Evaluate. Track factual F1, citation accuracy and token spend; iterate thresholds.
Pitfalls to avoid
Over-stuffing: adding more text than the context window harms the model's own recall.19
Uniform compression: fixed-ratio summarisation can erase dense regions; dynamic allocators like DAST fix this.20
Security blind spots: retrieval indices may leak data if ACLs mirror the raw store rather than the query domain.21
Checklist (related items)
- Allocate evidence tokens before writing system instructions
- Keep retrieval + safety blocks within 75 % of window
- Apply rerankers to cut noise
- Use compression only after measuring loss on a held-out validation set
- Log every query–context pair for future audits
Follow this sequence and a reasoning model will receive exactly the knowledge it needs—no more, no less—while prompt engineering can focus on clarity and voice rather than data delivery.