akshay-na/ai-orchestration-prompt-engineering

AI Orchestration & Prompt Engineering

Overview

Use AI systems as structured collaborators, not autocomplete tools. Every AI interaction should have clear boundaries, verifiable output, and human oversight. Trust nothing by default. Validate everything.

When to Use

• Delegating a task to a specialized AI agent or subagent.
• Designing prompts that require structured, reproducible output.
• Reviewing AI-generated code, architecture, or reasoning.
• When AI output seems plausible but unverified.
• When prompt iterations are not converging on useful results.
• When deciding what to delegate vs what to do manually.

When NOT to Use

• Simple completions where the output is trivially verifiable.
• Tasks where AI adds no leverage over direct implementation.

Core Competencies

| Competency | Key Question | |---|---| | Task delegation | Is the task well-scoped with clear success criteria? | | Structured output | Does the prompt enforce format, constraints, and boundaries? | | Reasoning validation | Can the AI's reasoning be independently verified? | | Hallucination detection | Are claims checked against source material or constraints? | | Prompt iteration | Is each refinement narrowing toward precision, not adding noise? | | Human oversight | Is there a review step before AI output is integrated? |

Practice Loop

1. Define task boundaries -- specify exactly what the AI should do, what it should not do, and what success looks like.
2. Require structured output -- enforce format (JSON, templates, checklists) to make output parseable and verifiable.
3. Cross-check assumptions -- verify factual claims, API references, and reasoning against primary sources.
4. Audit critically -- treat AI output as a draft from a junior engineer; review before merging.
5. Refine prompt structure -- tighten constraints, add examples, remove ambiguity with each iteration.

Failure Signals

Stop and reassess if you observe:

| Signal | Root Cause | |---|---| | Blind trust in AI output | No verification step; output accepted without review | | Unstructured responses | Prompt lacks format constraints; output is free-form prose | | AI-driven complexity | AI suggested abstractions or patterns that add no real value | | Poor reproducibility | Same prompt yields inconsistent results; lacks specificity | | Hallucinated references | AI cites APIs, libraries, or patterns that do not exist | | Delegation without criteria | Task given to AI with no definition of done or success metric |

Quick Reference

Prompt Design Checklist:

## Role & Context
- [ ] Agent role is explicit (e.g., "You are a security reviewer")
- [ ] Relevant context provided (codebase, constraints, domain)
- [ ] Scope is bounded (what to review, what to ignore)

## Task Specification
- [ ] Task is a single, clear instruction
- [ ] Success criteria defined
- [ ] Output format specified (template, schema, structure)
- [ ] Constraints stated (do NOT do X)

## Verification
- [ ] Output is independently verifiable
- [ ] Factual claims can be checked against sources
- [ ] Code suggestions can be tested
- [ ] Reasoning chain is traceable

## Iteration
- [ ] First result reviewed before accepting
- [ ] Ambiguous output triggers prompt refinement, not guessing
- [ ] Each iteration adds precision, not complexity

Delegation Decision Framework:

Delegate to AI when:
- Task is well-defined and output is verifiable
- Task benefits from broad pattern matching (search, review, comparison)
- Manual execution is tedious but verification is fast
- Output format can be constrained

Do NOT delegate when:
- Task requires judgment that cannot be verified from output alone
- Incorrect output has high cost and low detectability
- Domain knowledge is too specialized for the model
- Verification would take longer than doing it manually

Common Mistakes

| Mistake | Fix | |---|---| | Accepting AI output without review | Treat every output as a draft; verify before integrating | | Vague prompts expecting precise results | Add constraints, format requirements, and examples | | Using AI to generate code you cannot review | Only delegate code you can read, test, and understand | | Iterating prompts without tightening scope | Each iteration should reduce ambiguity, not add instructions | | No fallback when AI fails | Define what to do when output is unusable; have a manual path | | Trusting AI-cited references | Verify every API, library, or pattern reference against docs |