How to Chain AI Prompts Like a Senior Engineer

Autoregressive transformers attend to tokens sequentially. When you ask a model to simultaneously analyze 5 pages of unstructured text, categorize sentiment, extract key entities, and produce a perfectly formatted TypeScript schema, you are maximizing the probability of constraint decay.

Instead of asking the model to do everything in one shot, split the task into discrete sequential transforms. Each step has one clear objective, zero distraction, and a concise output contract.

Consider how Unix tools work: cat access.log | grep 404 | awk '{print $7}' | sort | uniq -c. Each command does one job cleanly. Your AI pipeline should follow the exact same architecture:

• Stage 1 (Extract): Parse raw input and extract raw facts/claims into a clean JSON array.
• Stage 2 (Verify): Cross-examine the extracted facts against ground truth or codebase references.
• Stage 3 (Transform): Apply formatting, tone, or code generation strictly to the verified facts.
• Stage 4 (Validate): Validate the final output against a rigid JSON schema or compiler test.

// Example: Clean prompt pipeline with typed schema intermediate
const rawNotes = await fetchMeetingNotes();

// Step 1: Extract action items (Zero conversational fluff)
const actions = await llm.generate({
  prompt: "Extract all commitments, assignees, and deadlines as a JSON array.",
  input: rawNotes,
  responseFormat: { type: "json_object" }
});

// Step 2: Categorize by department and risk
const prioritized = await llm.generate({
  prompt: "Assign priority (P0-P3) and department to each item based on impact.",
  input: actions
});

The greatest failure point between prompt stages is conversational drift — the model outputting 'Sure! Here is the JSON you requested:' before the JSON object. This breaks downstream parsers instantly.

Here are four production-grade commands from the SlashAI library designed specifically for multi-step prompt engineering and evaluation:

What happens when Stage 4 schema validation fails? Novice workflows crash or discard the result. Production systems catch the validation error and send the exact error back to the model in an automated repair turn:

try {
  const verified = TaskSchema.parse(JSON.parse(output));
  return verified;
} catch (err) {
  // Feed the parser error directly back to the model
  return await llm.generate({
    prompt: "Your previous JSON response violated our schema. Fix the errors listed below without altering correct fields.",
    input: { previousOutput: output, schemaErrors: err.issues }
  });
}

This single feedback mechanism increases task success rates across complex technical extractions from ~72% to over 96%.