Context Engineering With OpenAI: Instructions, Tools, and Memory

Context engineering with OpenAI means choosing the instructions, evidence, tools, and working state the model needs for its next action. Start by separating those inputs, then test whether they lead to an answer you can verify. A longer conversation alone does not tell you which project decisions remain current.

Tools like fenbs keep approved project decisions and progress outside one conversation, so a connected assistant can read the work again. The practical goal is a clear starting point: what to do, what to trust, and what still needs a person.

How do you organize your first OpenAI workflow?

1. Choose one repeatable task. For example, draft a release note from a small set of completed changes. Define the expected result before adding tools or more documents.
2. Write the stable behavior instructions. Specify the audience, required evidence, output format, and actions that need approval. Keep individual source documents separate from those instructions.
3. Assemble a small evidence packet. Include the relevant changes, their completion checks, and the approved release scope. Label missing evidence instead of asking the model to fill gaps.
4. Decide what survives the request. Keep conversation continuity in your application, and store approved project facts where people can correct them independently of the chat.
5. Run the task, inspect the evidence behind its answer, and save the remaining work. Repeat with one deliberately missing or conflicting source before expanding the workflow.

This is an input design exercise before it is a software architecture exercise. Our context engineering and prompt engineering comparison explains the broader distinction; here the focus is putting it into an OpenAI application.

Which instructions belong in each request?

The official OpenAI text generation documentation↗ (opens in a new tab) describes the Responses API, the interface an application uses to request model output. Its instructions parameter provides high-level guidance with priority over the request input. Developer messages also have priority over user messages.

One detail matters when chaining responses: instructions from a previous request are not carried forward merely because you pass previous_response_id. Supply the instructions that must apply to the current response. Treat that as part of constructing each request rather than relying on what the assistant appeared to remember.

For the release note example, stable instructions might require plain English, a source for every completed claim, and a separate list of uncertain items. The release name and selected changes belong in the task input. An old assistant answer saying a change shipped belongs in neither category until someone checks it.

Keep ownership visible. A person can approve a writing rule; a model can suggest one. Recording those choices in Decisions and rules in fenbs gives later connected sessions a place to check what was actually agreed.

How should tools supply evidence?

OpenAI supports built-in tools, function calling for your own application functions, and remote MCP servers, which expose outside tools through a standard connection. The OpenAI tools guide↗ (opens in a new tab) explains that configuration and call handling depend on the integration you use. Choose tools for specific information needs.

Before adding a search tool, write the question it should answer. “Find the approved release scope” is useful. “Read everything about this product” has no clear stopping point. Return the relevant passage with its source identity and current status, so the final answer can distinguish an approval from a suggestion.

Treat retrieved text as evidence to examine. A copied document may contain old directions, an unapproved proposal, or instructions meant for someone else. Your application should make the source boundary clear and restrict tool access appropriately. A sentence in a document should not grant permission to change the project.

What does conversation state remember?

The OpenAI conversation state guide↗ (opens in a new tab) describes response chaining and the Conversations API. A conversation can persist messages, tool calls, tool outputs, and other items under a durable identifier. That helps an application continue an interaction without rebuilding every previous turn manually.

Conversation continuity and an approved project record answer different questions. The former says what this interaction contained. The latter says what the team currently accepts and where the next session should begin. Do not treat a statement as approved merely because it is stored in the conversation.

For example, a release discussion might contain three possible dates and a later choice. Keep the discussion if it is useful, but give the current choice its own clear record. When another assistant takes over, provide that choice and the unfinished checks instead of asking it to reconstruct the agreement from every turn. A project record in fenbs keeps that current choice separate from the conversation.

How do you check that the context is working?

Make a small set of cases whose answers you can judge. Keep the model, task wording, and success criteria stable while changing one input at a time. Otherwise, an improved answer gives you little evidence about which change helped.

• Current evidence: the assistant includes only changes with completion evidence and cites the right source.
• Conflicting evidence: an old release list disagrees with the approved scope, and the assistant identifies the conflict.
• Missing evidence: a change lacks its final check, and the assistant labels it uncertain rather than announcing completion.
• Fresh conversation: the assistant can recover the current decision and next action without the original chat.

Score the actual requirement, such as whether an unverified change was presented as shipped. Fluent prose is a separate concern. Save the input set and result so you can rerun the same check after changing instructions or tools. For more complex action sequences, see context engineering for AI agents.

How to do this with fenbs

1. Create a free account, then add the project whose decisions your assistant needs. Start with one release or another bounded piece of work.
2. Add its tasks and record the decisions a person has approved. Keep unresolved choices open, and put continuing instructions in Decisions and rules.
3. Choose a supported connection from Integrations, such as ChatGPT or Codex CLI, and complete its browser sign-in. Ask the assistant to read project context before starting.
4. Save progress when the session ends: what changed, what was checked, what remains uncertain, and where the next session should start.

A custom OpenAI API application is a separate engineering integration. Only use fenbs through it if you implement and verify the appropriate authenticated tool connection; an OpenAI API account does not create that connection automatically. fenbs does not host models or build your retrieval system, so use application development tools when those are the work you need.

What should the next session receive?

Give the next session a compact account of the current goal, relevant decisions, source locations, and outstanding checks. Ask it to restate what it will do before giving it a consequential action. If it describes an old release scope, correct the starting record before continuing.

The same discipline is useful in an editor, as the Cursor context guide shows. Keep the work record independent of the particular interface, and use the context engineering hub when you need to explore a different part of the process.