Getting Started: Training an AI Assistant with Codex

Training an AI assistant starts with clear intent and a rapid prototype. Codex is an excellent entry point because it can translate intent into structure—drafting prompts, scaffolding tools, and generating synthetic examples—so you can focus on defining quality.

Define the north star

Begin by writing a one-paragraph charter that captures the assistant's audience, tone, and boundaries. Anchor every decision to this charter and keep it visible in your workspace. For this project, the assistant should be practical, concise, and transparent about limitations.

Capture seed workflows

List 5–10 high-value tasks the assistant should excel at. For each task, collect:

• Inputs: real user questions, context snippets, or configuration files
• Desired outputs: an ideal answer with reasoning, references, and any safety constraints
• Failure modes: what a bad answer looks like (hallucinations, missing steps, unsafe guidance)

These examples become the first evaluation set. Even a small set is enough to guide Codex toward your expectations.

Use Codex to draft the baseline prompt

Start with a system prompt that encodes the charter, tone, and formatting. Ask Codex to propose a structured prompt and then refine it manually. A simple template:

You are <assistant-name>, a concise AI assistant for <audience>.
Always:
- Ask for missing context in one sentence.
- Show a numbered plan before long answers.
- Cite tools or docs you relied on.
Avoid speculation; say "I don't know" when uncertain.

Pair this with 3–5 few-shot examples built from your seed workflows. Codex can help draft these examples quickly—treat them as prototypes you will edit for accuracy and tone.

Build the first evaluation loop

Before fine-tuning anything, run the baseline prompt against your seed examples:

1. Call Codex with the prompt and each input.
2. Score the responses manually using three criteria: correctness, actionability, and safety.
3. Capture issues in a short log (one line per failure) to drive revisions.

This loop surfaces where the prompt is unclear or where extra instructions are needed. Keep iterations tight: revise, rerun, and note the delta in quality.

Grow structured training data

Once the baseline is stable, expand the dataset:

• Synthesize more examples with Codex, then review them for accuracy.
• Source real transcripts from support channels or internal documentation, anonymizing sensitive details.
• Label outputs with short rationales (why the answer is good or bad). These labels make future fine-tuning or preference optimization faster.

Aim for diversity: edge cases, multilingual queries, and safety-sensitive prompts.

Plan for fine-tuning and tooling

When the prompt stabilizes and evaluations trend upward, plan the first model adaptation:

• Start with prompt-only updates to keep iteration fast.
• Move to fine-tuning once you have hundreds of high-quality, labeled interactions.
• Introduce tooling hooks (search, calculators, policy checkers) and teach the model when to call them through explicit examples.

Codex remains useful here: generate tool-call skeletons, draft unit tests for tool outputs, and propose guardrails that your application layer can enforce.

Next steps for this project

• Publish the charter and initial prompt for peer review.
• Automate the evaluation loop so every prompt change is scored against the seed set.
• Define success metrics (e.g., 85% correctness on the seed set, zero unsafe outputs) before expanding the scope.

This blogpost marks the starting point. With a Codex-bootstrapped baseline and a tight evaluation loop, we can iterate confidently toward a reliable, production-ready AI assistant.

Quick Starter Checklist

• Charter written and pinned.
• Seed workflows documented with desired outputs.
• Baseline prompt with 3–5 few-shot examples.
• Manual evaluation sheet with scoring rubric.
• Automation script to replay evaluations after each change.