avav25/prompt-engineering

Prompt Engineering

Comprehensive prompt engineering knowledge base. Provides actionable patterns, checklists, and guides for designing, securing, evaluating, and optimizing LLM prompts and agent systems.

When to Use

• Designing or reviewing prompt templates (system, developer, user prompts)
• Building tool calling schemas and structured output contracts
• Evaluating prompt quality — accuracy, safety, cost, latency
• Auditing LLM security against OWASP LLM Top 10
• Designing multi-agent orchestration and handoff protocols
• Optimizing prompt cost and latency
• Creating or reviewing Codex AI assets (rules, workflows, skills — all are prompts)
• Setting up prompt versioning and observability

When NOT to Use

• Implementing backend/frontend code (use software-engineer role + stack-specific role)
• Infrastructure and deployment (use devops-engineer role)
• Writing code tests (use qa-engineer role + test-strategy skill)
• Content writing (use content-writer role)
• Context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production AI checklists → use context-engineering skill

Key Concepts

Prompt as System

A prompt is not a string — it is a system composed of:

1. Instruction hierarchy: System prompt > Developer prompt > User prompt > Retrieved content
2. Context assembly: What enters the context window, in what order, with what priority
3. Output contract: Schema, format, constraints, error handling, fallback behavior
4. Tool interface: Available tools, their schemas, permissions, composition patterns
5. Guard rails: Safety filters, refusal policies, output validators
6. Versioning: Immutable versions, deployment tags, audit trail

Core Principles

1. Eval-first: Define how to measure before changing anything
2. Simplest technique: Zero-shot → few-shot → CoT → chaining. Escalate only when simpler fails
3. Explicit over implicit: Spell out constraints, output format, edge cases. Never assume the model "knows"
4. Separation of concerns: Instructions vs data vs examples — always delimited
5. Grounding: Prefer citations and verifiable data over unanchored claims
6. Least privilege: Minimal tool permissions per agent. HITL for high-impact actions
7. Cost awareness: Every token costs money and time. Compress, cache, route

Resource Files

| File | Contents | |---|---| | technique-guide.md | Technique selector overview and reading map | | technique-guide-core.md | Zero-shot, few-shot, CoT, self-consistency, constrained generation | | technique-guide-agentic.md | ToT, ReAct, chaining, reflection, RAG, meta-prompting, provider guidance | | prompt-template-patterns.md | Prompt structure overview and pattern index | | prompt-template-foundations.md | Delimiters, system prompt architecture, few-shot formatting, CoT formatting | | prompt-template-contracts.md | Output schemas, tool schemas, prompt registry, provider-specific adaptations | | security-checklist.md | OWASP LLM Top 10 mapped to prompt-level mitigations with checklist | | eval-and-testing-guide.md | Eval workflow overview and decision map | | eval-datasets-and-graders.md | Dataset curation, grader types, grader selection | | eval-deployment-and-monitoring.md | Regression gates, A/B testing, deployment rollout, monitoring |

Integration

• Follows rules: prompt-engineer role (prompt system architecture, security, eval-first quality)
• Used by workflows: ai-assets skill (all assets are prompts), feature-dev skill (AI features), code-review skill (prompt quality review)
• Companion skills: context-engineering skill (context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production checklists), asset-validation skill (AI asset format validation), code-review skill (review checklists)
• Collaborates with roles: software-engineer role (prompt integration), qa-engineer role (prompt regression tests), product-manager role (success metrics)