Provide GPT 5.3 Codex with the smallest set of context that makes the task deterministic: the relevant files, interfaces, constraints, and the evidence of failure or desired behavior. The model does better with focused, high-signal context than with a huge dump. In coding tasks, context usually needs to answer: “What are the inputs/outputs?”, “Where is the logic implemented?”, “What constraints must be preserved?”, and “How will we know it works?” If you can’t answer those, the model will either guess or ask questions—both of which slow you down. The best context is what a human would open in their editor before making the change.
A practical “context checklist” you can standardize across your team:
For bug fixes
Repro steps (exact command, input payload, environment)
Error output (stack trace, logs, failing test output)
The suspected file(s) and entrypoint function(s)
Existing test coverage around the failing path
For feature work
The spec (acceptance criteria, edge cases)
Current interfaces (API routes, function signatures, schemas)
Where similar behavior exists (reference implementation file path)
Non-goals (explicitly what not to change)
For refactors
Scope boundaries (which directories are in-scope)
Performance constraints (latency, memory, complexity)
Compatibility constraints (public API stability)
Style rules (lint config, formatting, naming conventions)
Also add an “authority hierarchy”: if docs contradict code, which wins? For open-source projects, you might set: “Prefer current code behavior; docs may lag. If mismatch, flag it.”
For documentation-heavy tasks and Q&A, the best context is usually not raw text pasted into the prompt—it’s retrieved and curated snippets. Index your docs, FAQs, and changelogs into a vector database such as Milvus or Zilliz Cloud, then retrieve the top relevant chunks with metadata filters like version and language. Provide those chunks with stable IDs and URLs. This keeps prompts compact, improves grounding, and makes the model’s output auditable (“it used these sources”). It also helps you scale the system: you update content by re-indexing, not by re-engineering prompts.