RAG Decision Analysis

“Build what you need. Not what sounds impressive.”

This document explains the decision to use file-based context management instead of Retrieval-Augmented Generation (RAG).

The Decision

Choice: File-based context management (upload files to AI Projects)

Not: RAG with vector databases and semantic search

Rationale: Current scale doesn’t justify RAG overhead

Revisit: August 2026 or when triggers fire

What is RAG?

Retrieval-Augmented Generation separates:

• Storage: Knowledge in a vector database
• Retrieval: Semantic search finds relevant chunks
• Generation: LLM uses only what’s retrieved

How RAG Works

Document → Chunking → Embedding → Vector Store
                                       ↓
Question → Embedding → Similarity Search
                           ↓
             Retrieved Chunks + Question → LLM → Response

RAG Advantages

• Handles unlimited document sizes
• Only relevant content loads
• Real-time updates without re-upload
• Scalable to thousands of documents

RAG Costs

• Vector database hosting: $20-100/month
• Embedding API calls: $10-50/month
• Setup complexity: 2-3 days
• Ongoing maintenance: Hours/month

Why File-Based Is Better (For Now)

Our Current Scale

File-Based Handles Our Needs

Context Windows Are Sufficient

Modern LLM context windows:

• GPT-4: 128K tokens (~320KB)
• Claude: 200K tokens (~500KB)
• Gemini: 2M tokens (~5MB)

Our active documentation (~600KB) fits in Claude or Gemini. GPT-4 works with selective loading.

The “Build It Right” Principle

“Always build it right, for the future, the first time.”

How File-Based IS Building Right

1. Architecture supports migration

   • Markdown files → RAG-ready format
   • Structured documentation → Easy to chunk
   • Clear hierarchies → Metadata-friendly

2. No premature optimization

   • RAG now = infrastructure we won’t use
   • File-based now = solving actual problems
   • Migrate when needed = right-sized solution

3. Cost efficiency

   • 6 months at $0 vs $300-600 for RAG
   • Same capability for current scale
   • Upgrade path preserved

When We’ll Need RAG

Trigger 1: Archive Search Frequency

Current: 1-2 searches/week (manual is fine) Trigger: 10+ searches/week (automation needed)

Trigger 2: Team Expansion

Current: Single user Trigger: 2+ people needing consistent AI access

Trigger 3: Documentation Growth

Current: ~600KB active Trigger: Active docs exceed context windows

Trigger 4: Cross-Document Synthesis

Current: Rare Trigger: Frequent need for AI to synthesize across dozens of docs

Migration Path (When Ready)

Recommended Stack

Migration Steps

1. Export context management files
2. Chunk documents (~500 tokens)
3. Embed via OpenAI API
4. Store in vector DB
5. Configure retrieval pipeline
6. Test retrieval quality
7. Integrate with AI platforms

Estimated effort: 2-3 days

Current System Performance

What We’ve Achieved

System Works

The file-based approach successfully:

• Maintains facts across sessions
• Coordinates multiple AI agents
• Prevents error propagation
• Enables session continuity

No evidence of capability gaps that RAG would solve.

Decision Summary

The Slow-Is-Smooth Principle

“Slow is smooth, and smooth is fast.”

The smooth path:

1. Now: File-based (working)
2. Q2 2026: Evaluate triggers
3. When needed: Implement RAG

The rough path:

1. Now: Build RAG (unused)
2. Next 6 months: Maintain unused infrastructure
3. Ongoing: Pay for low-volume queries

Smooth wins.

Right-sized solutions. Upgrade when warranted.

FOR THE KEEP!