# Prompt Engineering

> Part of the AI Garage Jargons glossary: https://aigarage.in/jargons (all terms: https://aigarage.in/jargons.md)

**Category:** Prompting
**Tags:** beginner, instructions, evaluation, quality

The iterative practice of designing instructions, context, examples, and output constraints so a model performs a defined task more reliably.

**In plain terms:** It is writing a clear handoff for the AI, then testing the results and improving the handoff when it fails.

**Use cases:**
- Consistent support-ticket summaries
- Structured extraction and classification
- Task-specific AI workflows

**Why it matters:** A production prompt defines the job, supplies relevant evidence, and makes the expected result inspectable. Testing it on representative inputs reveals where instructions or context still fail.

**Why now:** Model families and even snapshots can respond differently to the same prompt. Current platform guidance therefore pairs prompt iteration with pinned model versions and repeatable evaluations instead of treating one successful response as proof of reliability.

**AI Garage example:** For an AI Garage build-workbook workflow, the prompt could identify the source session, define the required section structure, distinguish evidence from interpretation, and include a small evaluation set that checks whether every step has an action, reason, and observable result.

**Mental model:** Treat a prompt as a versioned task specification, not a magic phrase. It combines the goal, relevant context, constraints, examples, and output shape. Representative evaluations show whether that specification works and where the next revision should focus.

**Builder scenario: Make support summaries testable**

- **Context:** A support workflow asks a language model to turn incoming tickets into a short summary with an issue type, urgency, and recommended next action.
- **Failure:** A vague request such as 'summarize this ticket' produces changing formats, occasionally omits urgency, and invents a next action when the ticket lacks enough evidence.
- **Intervention:** Define the required fields and evidence boundary, provide representative examples, request a structured output, and run the prompt against a fixed evaluation set after each change.
- **Outcome:** The workflow can validate every response for the required fields, compare prompt versions on the same cases, and route unsupported recommendations for review instead of silently accepting them.

**Common misconceptions:**
- **Claim:** Prompt engineering is finding one clever phrase that makes the model reliable.
  **Correction:** Reliable prompting is an iterative design and evaluation process. Clear instructions help, but model choice, context, tools, validation, and representative tests still matter.

**Visual model (sequence):** The prompt improves through a measured loop. A polished first response is not a substitute for repeatable evaluation.

**Visual description:** Prompt engineering moves from defining success to composing instructions and context, testing representative cases, and revising from observed failures.

**Related concepts:** [Context Window](https://aigarage.in/jargons/context-window), [RAG (Retrieval-Augmented Generation)](https://aigarage.in/jargons/rag), [Fine-tuning](https://aigarage.in/jargons/fine-tuning)

**Video explainer:** [Prompt Engineering: a testable handoff, not a magic phrase](https://nqqbubjesbyuwrdhqxnk.supabase.co/storage/v1/object/public/aigarage-public-media/jargons/prompt-engineering/v2/prompt-engineering.mp4) (43 seconds)

See how a vague support-summary request becomes a versioned task specification with explicit evidence, output constraints, representative tests, and revisions based on observed failures.

**Published:** 2026-10-12T11:59:37+05:30
**Captions:** [English](https://aigarage.in/jargons/prompt-engineering/captions.vtt)
**Narration:** generated

**Transcript:** A support team asks a model to summarize a ticket.

One answer is a paragraph. Another leaves out urgency.

A third invents a next step nobody requested.

Prompt engineering starts by replacing that vague request with a clear task.

Say which fields are needed, and what evidence the model may use.

Show one example, and the shape the answer should take.

Say what to do when the evidence is missing.

Then run the prompt on the same set of real tickets.

Look at where it fails, change the prompt, and test again.

Magic phrases fail. One good answer proves little.

Prompt engineering is a measured loop, not a lucky guess.

**Last researched:** 2026-10-11

**Sources:**
- [OpenAI API - Prompt engineering](https://developers.openai.com/api/docs/guides/prompt-engineering)
- [OpenAI API - Model optimization](https://developers.openai.com/api/docs/guides/model-optimization)

**URL:** https://aigarage.in/jargons/prompt-engineering