Context engineering: give AI the right working information

01

Context is the model’s temporary workspace

The model can only reason from the information available in the current interaction and any tools the product explicitly provides. Context engineering is the practice of placing the right information in that workspace while excluding noise, secrets, and irrelevant history.

Think like a project lead preparing a handoff. A colleague needs the objective, source of truth, constraints, examples, definitions, and current state. They do not need every message ever written about the project.

02

Identify the minimum sufficient context

Begin with the decision or deliverable. Work backward to the facts necessary to produce it. Separate must-have context from helpful background and optional reference material.

Excess context can reduce attention to the important parts and introduce contradictions. If a source is long, point to relevant sections or provide a faithful extract. Preserve links or identifiers so a human can verify the source later.

• Objective and intended user
• Definitions and terminology
• Authoritative source material
• Constraints, policies, and exclusions
• Examples of acceptable and unacceptable output
• Current state and known decisions

03

Give sources a hierarchy

Projects often contain a policy, a specification, meeting notes, and informal suggestions. Label which source wins when they conflict. A simple order such as policy, approved specification, current data, then working notes can prevent accidental use of outdated information.

Include dates and versions for time-sensitive material. Ask the model to report contradictions rather than quietly selecting whichever statement appears last.

04

Structure the context for the task

Use headings such as Goal, Audience, Sources, Constraints, Examples, and Required Output. For several documents, label each source and explain its role. For data, define columns and units. For code, include the expected behavior, smallest relevant files, error, and recent change.

Structure is not decoration. It reduces the chance that source text will be mistaken for an instruction and helps you see what information is missing.

05

Protect private and sensitive information

Do not paste credentials, access tokens, private keys, payment details, health information, or personal identifiers into a prompt unless an approved system and process explicitly permit it. Minimize or anonymize customer and employee data.

Replace secrets with descriptive placeholders. Provide only the fields needed for the task. If a use case repeatedly requires sensitive information, solve it through governance and secure system design rather than individual prompt habits.

06

Maintain context as a reusable asset

For recurring work, keep stable instructions separate from changing task inputs. A reusable template may contain the process and review standard, while each run supplies current data, source documents, and the desired output.

Assign an owner and review date to important context packs. Remove outdated policies and examples. A prompt with current context is more reliable than a sophisticated prompt attached to obsolete information.