Retrieval Playbook For AI

Retrieval Playbook for AI

Skill Profile

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Playbook for deciding what context to retrieve for AI - select only what's needed, exclude what's irrelevant, and prioritize appropriately.

Why This Matters

• Relevance: Only include what's relevant
• Token efficiency: Don't waste tokens on irrelevant information
• Better results: AI can focus better
• Faster: Less context = faster

Core Concepts & Rules

1. Decision Tree

Task received
    ↓
Is it code-related?
    ├─ Yes → Retrieve code files
    └─ No → Skip code
          ↓
Need current state?
    ├─ Yes → Include recent changes
    └─ No → Skip history
          ↓
Need examples?
    ├─ Yes → Include 1-2 examples
    └─ No → Skip examples
          ↓
Assemble context

2. Retrieval Rules

Rule 1: Only Direct Dependencies

❌ Include entire codebase
✅ Include only files directly related

Example:
Task: Fix bug in auth.ts
Include:
- auth.ts (the file)
- types.ts (if auth.ts imports it)
- config.ts (if auth.ts uses it)

Don't include:
- unrelated files
- test files (unless debugging tests)
- documentation

Rule 2: Snippets Over Full Files

❌ Include entire 500-line file
✅ Include 20-line relevant function

Example:
Task: Fix validateToken function
Include:
- validateToken function (lines 45-60)
- Related types (5 lines)
- Helper functions used (10 lines)

Total: ~35 lines vs 500 lines
Savings: 93%

Rule 3: Recent Over Old

✅ Last 3 commits
❌ Full git history

✅ Current implementation
❌ Deprecated code

Rule 4: Examples Only When Needed

Include examples when:
✓ New pattern/concept
✓ Complex logic
✓ User explicitly asks

Skip examples when:
✗ Simple CRUD
✗ Standard patterns
✗ Self-explanatory

3. Context Prioritization

Priority 1: Critical (Always Include)

- File with bug/feature
- Error messages
- Relevant types/interfaces
- Direct dependencies

Priority 2: Important (Include if Space)

- Related functions
- Configuration
- Recent changes
- Test cases

Priority 3: Nice-to-Have (Usually Skip)

- Full documentation
- Examples
- Comments
- Historical context

Inputs / Outputs / Contracts

Inputs

• Task or problem description
• Code repository
• File system
• Documentation
• Error messages and stack traces

Outputs

• Optimized context for AI
• Relevant code snippets
• Prioritized information
• Token-efficient retrieval
• Quality maintained

Contracts

• Input Validation: All inputs must be valid task descriptions
• Output Format: Context follows retrieval playbook standards
• Token Budget: Retrieved context respects configured limits
• Relevance Guarantee: Only directly relevant information included
• Priority Enforcement: Critical information prioritized over nice-to-have

Skill Composition

• Depends on: context-pack-format, anti-bloat-checklist
• Compatible with: prompt-library-minimal, vector-database
• Conflicts with: None
• Related Skills: summarization-rules-evidence-first, retrieval-quality

Quick Start

Quick Checklist

Before retrieving, ask:
☐ Is this directly related to task?
☐ Can I use a snippet instead of full file?
☐ Is this the most recent version?
☐ Will AI actually use this?
☐ Am I under my token budget?

If any answer is "no", reconsider including it.

Assumptions

• AI models have token limits
• Context needs to be efficient
• Relevance is more important than completeness
• Token cost is a consideration
• Quality must be maintained

Compatibility

• AI Models: GPT-4, Claude, etc.
• Repositories: Git, SVN, etc.
• File Types: Code, docs, configs
• Languages: All programming languages

Test Scenario Matrix

Technical Guardrails

Retrieval Requirements

• All context MUST be directly relevant
• All context MUST use snippets over full files
• All context MUST be recent and accurate
• All context MUST follow priority levels

Filtering Requirements

• All irrelevant files MUST be excluded
• All outdated information MUST be excluded
• All redundant information MUST be excluded
• All unnecessary examples MUST be excluded

Quality Requirements

• All critical info MUST be included
• All context MUST be accurate
• All context MUST be complete for task
• All context MUST be well-organized

Security Threat Model

Threats Addressed

• Token waste: Efficient retrieval
• Information overload: Prioritization
• Context overflow: Size limits enforced
• Quality degradation: Structured retrieval

Mitigation Strategies

• Use decision tree for retrieval
• Implement filtering by relevance
• Follow priority levels
• Monitor token usage
• Validate context quality

Domain-Specific Modules

Context Retriever Module

export interface ContextRetriever {
  retrieve(task: string, repository: string): Promise<ContextPack>;
}

export class SmartRetriever implements ContextRetriever {
  async retrieve(task: string, repository: string): Promise<ContextPack> {
    const files = await this.getFiles(repository);
    const relevant = this.filterByRelevance(files, task);
    const prioritized = this.prioritize(relevant);
    return this.assemble(prioritized);
  }

  private async getFiles(repo: string): Promise<File[]> {
    // Get files from repository
    return [];
  }

  private filterByRelevance(files: File[], task: string): File[] {
    // Filter by relevance score
    return files.filter(f => this.calculateRelevance(f, task) > 0.7);
  }

  private prioritize(files: File[]): File[] {
    // Sort by priority
    return files.sort((a, b) => b.priority - a.priority);
  }

  private assemble(files: File[]): ContextPack {
    // Assemble context pack
    return {
      summary: this.generateSummary(files),
      code: this.extractSnippets(files),
      types: this.extractTypes(files),
    };
  }
}

Relevance Scorer Module

export function calculateRelevance(file: File, task: string): number {
  let score = 0;

  // Direct match
  if (file.path.includes(task)) score += 0.5;

  // Recent changes
  if (file.lastModified > sevenDaysAgo) score += 0.2;

  // File size (smaller is better)
  score += Math.max(0, 1 - file.lines / 500);

  return Math.min(1, score);
}

Token Budget Module

export interface TokenBudget {
  maxTokens: number;
  usedTokens: number;
  remainingTokens: number;
}

export function checkBudget(context: ContextPack, budget: number): TokenBudget {
  const tokens = estimateTokens(context);
  return {
    maxTokens: budget,
    usedTokens: tokens,
    remainingTokens: budget - tokens,
  };
}

Release, Rollback & Ops Notes

Release Process

1. Define retrieval strategies
2. Implement filtering logic
3. Create prioritization rules
4. Test with sample tasks
5. Deploy to production
6. Monitor retrieval efficiency
7. Adjust strategies as needed

Rollback Procedure

1. Revert retrieval changes
2. Restore previous retrieval logic
3. Monitor quality impact
4. Roll back if necessary

Operational Procedures

• Retrieval monitoring: Track token usage
• Quality checks: Verify AI comprehension
• Strategy updates: Improve based on feedback
• Budget management: Enforce token limits

Code Quality & Documentation

Retrieval Standards

• Use decision tree for selection
• Filter by relevance and recency
• Prioritize critical information
• Use snippets over full files
• Track token savings

Documentation Requirements

• Document retrieval strategies
• Provide examples for each rule
• Include prioritization guidelines
• Track token savings
• Document best practices

Agent Directives & Error Recovery

Agent Behavior Rules

1. Always use decision tree for retrieval
2. Always filter by relevance
3. Always prioritize critical information
4. Always use snippets over full files
5. Always track token usage

Error Recovery Patterns

Agent Prompt Pack

Retrieval Prompts

"Retrieve context for {task} that:
- Uses decision tree for selection
- Filters by relevance and recency
- Prioritizes critical information
- Uses snippets over full files
- Stays under token budget"

Filtering Prompts

"Filter files for {task} that:
- Includes only directly related files
- Uses snippets over full files
- Excludes outdated information
- Excludes redundant information
- Prioritizes by relevance"

Prioritization Prompts

"Prioritize context for {task} that:
- Puts critical info first
- Includes important info if space allows
- Excludes nice-to-have items
- Follows hierarchy levels
- Maintains quality"

Definition of Done

Retrieval is complete when:

• Decision tree followed
• Only direct dependencies included
• Snippets used over full files
• Recent information prioritized
• Examples only when needed
• Token budget respected
• Quality maintained
• Metrics tracked
• Best practices followed

Anti-patterns

1. Kitchen sink approach: Include everything just in case
2. No filtering: Retrieve all files that mention keyword
3. Full file dumps: Including entire files when snippets suffice
4. No prioritization: All information at same level
5. Old information: Including deprecated or outdated code
6. No examples: Missing examples when needed
7. Too many examples: Including redundant examples
8. No hierarchy: Critical and nice-to-have treated equally

Reference Links

• OpenAI Tokenizer
• Prompt Engineering Guide
• Context Optimization Best Practices

Versioning & Changelog

v1.0.0 (2025-02-15)

• Initial release of Retrieval Playbook for AI skill
• Decision tree for retrieval
• Retrieval rules (4 rules)
• Context prioritization (3 levels)
• Retrieval strategies by task type
• Smart filtering (recency, relevance, size)
• Context assembly
• Metrics to track
• Anti-patterns
• Quick checklist

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