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When RAG Isn't the Answer: An Honest Assessment

We turn down projects that don't benefit from RAG. Here's why.

Our Philosophy

"We're RAG experts, but we're not RAG evangelists. Not every problem needs RAG, and honesty builds better partnerships."

🎯 Why This Page Exists

Too many vendors push their solution for everything. We help you make the right choice, even if it's not us.

📚 What You'll Learn

  • 8 scenarios where RAG is overkill or wrong approach
  • Better alternatives for each scenario
  • How to assess if your use case needs RAG

Scenarios Where RAG Isn't Needed

When simpler solutions work better

1. Your Data is Small (<100 Pages)

📋 Situation:

Startup with 50-page product documentation wants AI chatbot

❌ Why Not RAG:

  • Can paste entire documentation into system prompt (50 pages ≈ 40K tokens)
  • Context window is sufficient
  • No retrieval needed
  • Much simpler to implement

✅ Better Alternative:

  • Simple prompt engineering with full context
  • Cost: $0.01 per query vs $0.05 for RAG infrastructure
  • Setup time: 1 day vs 4 weeks

🔄 When to Reconsider:

  • If you grow to 500+ pages
  • If you need multi-language support
  • If docs update daily

2. You Need General Knowledge (Not Domain-Specific)

📋 Situation:

Building a general Q&A chatbot about world history, science, coding

❌ Why Not RAG:

  • Base LLMs already trained on this content
  • Adding retrieval adds latency without improving accuracy
  • You'd be retrieving from sources LLM already knows

✅ Better Alternative:

  • Use base GPT-4, Claude, or Llama directly
  • Add prompt engineering for format/style
  • No infrastructure needed

💡 Example Query:

"Explain photosynthesis"

  • Base LLM: Excellent answer, <1 second, $0.001
  • RAG: Same answer, 2 seconds, $0.02 (unnecessary overhead)

3. You Need to Change Model Behavior/Style

📋 Situation:

Want LLM to write in your brand voice, follow specific format, adopt persona

❌ Why Not RAG:

  • This is about how the model responds, not what it knows
  • Retrieval doesn't change response style
  • You need behavior modification, not knowledge addition

✅ Better Alternative:

  • Fine-tuning on style examples (if you have 1K+ examples)
  • Or system prompts with few-shot examples
  • Or instruction-tuned base model

💡 Example:

Customer service bot that needs to:

  • Always be empathetic
  • Use first names
  • Follow 3-step: acknowledge → resolve → follow-up

This is prompt engineering territory, not RAG

4. Your Data is Highly Structured (SQL-Queryable)

📋 Situation:

Sales data in SQL database, need to answer "What were Q3 revenues?"

❌ Why Not RAG:

  • Vector search on structured data is inefficient
  • SQL is perfectly designed for this
  • Natural language → SQL is the right pattern

✅ Better Alternative:

  • Text-to-SQL systems (using LLMs to generate SQL)
  • Traditional BI tools (Tableau, PowerBI)
  • Graph databases (for relationship queries)

🎯 When RAG Makes Sense:

  • If you have SQL data + unstructured documents (hybrid)
  • If queries need both structured and unstructured retrieval

5. You Need Real-Time Dynamic Data

📋 Situation:

Stock prices, weather, live sports scores, current news

❌ Why Not RAG:

  • RAG retrieves from indexed documents (minutes/hours/days old)
  • Real-time data needs API calls, not vector search
  • By the time you index it, it's outdated

✅ Better Alternative:

  • LLM function calling with live APIs
  • Agent systems with tool usage (can call APIs)
  • Traditional API integration

💡 Example:

"What's Tesla stock price right now?"

  • RAG: Retrieves yesterday's article (wrong answer)
  • API: Calls live stock API (correct answer)

6. Extreme Latency Requirements (<100ms)

📋 Situation:

High-frequency trading decisions, real-time medical alerts, autonomous vehicle decisions

❌ Why Not RAG:

  • Vector search adds 50-200ms latency
  • LLM inference adds 500-2000ms
  • Total: 550-2200ms (too slow for <100ms requirement)

✅ Better Alternative:

  • Traditional rule-based systems
  • Classical ML models (decision trees, random forests)
  • Cached precomputed answers

🔄 When to Reconsider:

  • If you can accept 1-2 second latency (most enterprise use cases)
  • If you can precompute common queries

7. Budget Constraints (<$25K Total Budget)

📋 Situation:

Small business or startup with limited funds

❌ Why Not RAG:

  • Minimum RAG implementation: $50K-75K
  • Infrastructure: $2K-5K/month ongoing
  • If budget is <$25K, can't afford proper RAG

✅ Better Alternatives:

  • Off-the-shelf solutions (Intercom AI, Zendesk AI)
  • Simple prompt engineering approaches
  • No-code RAG tools (less customization but cheaper)

🔄 When to Reconsider:

  • When you can allocate $50K+ for AI infrastructure
  • When ROI justifies investment (saving $200K+/year)

8. You Have Insufficient Data Quality

📋 Situation:

Documents are poorly formatted, inconsistent, heavily duplicated, or error-filled

❌ Why Not RAG:

  • Garbage in, garbage out
  • RAG will retrieve bad documents → bad answers
  • Need data cleaning first

✅ Better Approach:

  1. Phase 1: Data quality assessment & cleaning (2-4 weeks)
  2. Phase 2: Then implement RAG (6-8 weeks)

🚩 Red Flags:

  • OCR errors in 30%+ of documents
  • No consistent structure/metadata
  • Heavy duplication (same content in 10+ files)
  • Contradictory information across sources

The "RAG Readiness Checklist"

Assess if your organization is ready for RAG implementation

Assessment Questions:

Check all that apply:

Scoring:

  • 7-8 checks: RAG is highly recommended
  • 5-6 checks: RAG likely beneficial, schedule consultation
  • 3-4 checks: Consider simpler alternatives first
  • 0-2 checks: RAG probably not right fit currently

Our Honest Recommendations

When we turn down projects and why

🚫 When We Turn Down Projects

We've declined projects when:

  • Client had 20-page FAQ (told them: use prompt engineering)
  • Budget was $15K (told them: not enough for quality RAG)
  • Data was 90% duplicated (told them: clean data first)
  • Need was style modification only (told them: try fine-tuning)

🤝 Why We Do This

  • Long-term relationships over quick sales
  • Our reputation depends on successful projects
  • We'd rather advise on alternatives than take money for wrong solution

📈 What Happens Next

  • If RAG isn't right, we'll tell you what IS right
  • We can refer you to appropriate solutions
  • We're happy to consult on alternative approaches
  • When you're ready for RAG later, we're here

Not Sure If RAG Is Right For You?

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