RLM Patterns Library

This skill contains proven patterns for recursive document analysis, derived from the RLM paper and empirical testing.

Core Patterns

1. Peek Before Processing

When: Starting any document analysis Purpose: Understand structure before committing resources

python

# Always start with a peek
preview = document[:2000]  # First 2000 chars
structure = analyze_structure(preview)

# Make informed decision
if structure.type == "code":
    strategy = "file_by_file"
elif structure.type == "academic_paper":
    strategy = "section_by_section"

Cost: ~50 tokens Benefit: Prevents wasted analysis on inappropriate strategies

2. Partition and Map

When: Document has clear boundaries (chapters, files, modules) Purpose: Parallel analysis of independent sections

python

# Partition
sections = split_by_boundaries(document)

# Map (spawn Workers)
analyses = []
for section in sections:
    result = spawn_worker(f"Analyze: {section.title}", section.content)
    analyses.append(result)

# Reduce
final_analysis = synthesize(analyses)

Best for: Codebases, multi-chapter documents, collections Parallelization: High (sections are independent)

3. Regex-Based Filtering

When: Searching for specific patterns or keywords Purpose: Reduce token consumption by filtering irrelevant content

python

import re

# Define patterns
patterns = {
    "security": r"(auth|token|password|crypto|encrypt)",
    "errors": r"(error|exception|panic|unwrap)",
    "async": r"(async|await|future|tokio)"
}

# Filter content
relevant_sections = []
for name, pattern in patterns.items():
    matches = re.finditer(pattern, document, re.IGNORECASE)
    for match in matches:
        # Extract surrounding context (±500 chars)
        start = max(0, match.start() - 500)
        end = min(len(document), match.end() + 500)
        relevant_sections.append({
            "type": name,
            "content": document[start:end],
            "match": match.group()
        })

# Analyze only relevant sections
for section in relevant_sections:
    analyze_section(section)

Token savings: 70-90% for targeted queries Best for: Finding specific patterns, security audits, API documentation

4. Verification Loop

When: High-stakes analysis (security, legal, financial) Purpose: Increase confidence through multiple passes

python

# Pass 1: Quick scan
candidates = quick_scan(content)

# Pass 2: Verify each candidate
verified = []
for candidate in candidates:
    if verify_finding(candidate, content):
        verified.append(candidate)

# Pass 3: Deep analysis of verified
for item in verified:
    detailed_analysis = deep_analyze(item)
    if detailed_analysis.confidence > 0.8:
        report_finding(detailed_analysis)

Cost: 3x base analysis Benefit: 90%+ reduction in false positives

5. Hierarchical Decomposition

When: Complex nested structures (class hierarchies, org charts) Purpose: Understand relationships and dependencies

python

# Build hierarchy
root = parse_structure(document)

# Recursive descent
def analyze_node(node, depth=0):
    analysis = {
        "name": node.name,
        "direct_analysis": analyze_content(node.content),
        "children": []
    }

    for child in node.children:
        if depth < max_depth:
            child_analysis = analyze_node(child, depth + 1)
            analysis["children"].append(child_analysis)

    return analysis

result = analyze_node(root)

Best for: Inheritance trees, directory structures, nested configurations

6. Iterative Refinement

When: Initial analysis reveals need for deeper investigation Purpose: Progressive deepening based on findings

python

# Phase 1: High-level overview
overview = analyze_overview(document)

# Phase 2: Identify interesting sections
hotspots = identify_hotspots(overview)

# Phase 3: Deep dive into hotspots
detailed_findings = []
for hotspot in hotspots:
    if hotspot.complexity > threshold:
        # Spawn Worker for detailed analysis
        detail = spawn_worker("Deep analysis", hotspot.content)
        detailed_findings.append(detail)

# Phase 4: Synthesis
return synthesize_findings(overview, detailed_findings)

Adaptive: Depth varies based on complexity Cost-effective: Only analyzes what's necessary

7. State Machine Pattern

When: Analysis spans multiple sessions (very large documents) Purpose: Resumable analysis with checkpointing

python

# Load or initialize state
state = load_state() or {
    "phase": "structure",
    "sections_completed": 0,
    "total_sections": 0,
    "results": []
}

# Execute current phase
if state["phase"] == "structure":
    structure = analyze_structure()
    state["total_sections"] = len(structure.sections)
    state["phase"] = "analysis"
    save_state(state)

elif state["phase"] == "analysis":
    section = get_next_section(state["sections_completed"])
    result = analyze_section(section)
    state["results"].append(result)
    state["sections_completed"] += 1

    if state["sections_completed"] >= state["total_sections"]:
        state["phase"] = "synthesis"
    save_state(state)

elif state["phase"] == "synthesis":
    final_report = synthesize(state["results"])
    clear_state()
    return final_report

Resumable: Can pause/resume anytime Fault-tolerant: Survives crashes/disconnections

Pattern Selection Guide

Document Type	Recommended Pattern	Reason
Large codebase (>1M tokens)	Partition + Verification	Files are independent, quality critical
Academic paper (50-200K)	Hierarchical Decomposition	Clear section structure
Legal document (100-500K)	Regex Filter + Verification	Need specific clauses, high accuracy
API documentation (200K)	Regex Filter + Partition	Search-driven, many independent sections
Novel/book (500K+)	State Machine	Too large for single session
Technical spec (100K)	Iterative Refinement	Complexity varies by section

Cost Optimization Strategies

1. Early Filtering

Filter irrelevant content before spawning subagents.

Bad:

python

for file in all_files:  # 1000 files
    analyze_file(file)  # $10 total

Good:

python

relevant_files = [f for f in all_files if is_relevant(f)]  # 100 files
for file in relevant_files:
    analyze_file(file)  # $1 total

2. Response Caching

Hash (task + context) and cache results.

python

cache_key = hash(f"{task}{json.dumps(context)}")
if cached_result := cache.get(cache_key):
    return cached_result

result = perform_analysis()
cache.set(cache_key, result, ttl=24*3600)
return result

3. Incremental Processing

Stop early if no issues found.

python

chunks = split_into_chunks(document)
for chunk in chunks:
    issues = scan_for_issues(chunk)
    if not issues:
        continue  # Skip deep analysis

    deep_analysis = analyze_deeply(chunk)
    report_findings(deep_analysis)

Anti-Patterns (Avoid)

❌ Exhaustive Processing

python

# Bad: Processes everything
for line in document.split('\n'):
    analyze_line(line)  # Millions of API calls

❌ Premature Recursion

python

# Bad: Recurses on trivial content
if len(section) > 0:
    spawn_worker(section)  # Even for 1 line

❌ No Early Exit

python

# Bad: Continues even when done
for section in sections:
    analyze(section)  # Should stop when answer found

❌ Context Bloat

python

# Bad: Includes entire document in every context
context = {
    "full_document": document,  # 1M tokens repeated
    "section": current_section
}

Quick Reference

Starting analysis? → Peek Before Processing Clear boundaries? → Partition and Map Searching for patterns? → Regex-Based Filtering High stakes? → Verification Loop Nested structure? → Hierarchical Decomposition Evolving analysis? → Iterative Refinement Very large doc? → State Machine Pattern

Always consider: Cost, accuracy, time, and resumability.