RAG IS
OVERRATED
Here's What You Actually Need
⚠️ CONTRARIAN TAKE
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source code
and
production patterns
at: community.nachiketh.in
The Test
Can you solve your problem with:
Structured
Few-Shot
Examples
Chain-of-Thought
Prompting
Function Calling
+
Clear Schemas
IF YES → YOU DON'T NEED RAG
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source code
and
production patterns
at: community.nachiketh.in
RAG Adds...
🏗️
Infrastructure Complexity
Vector databases, embedding pipelines, deployment overhead
💰
Database Costs
Pinecone, Weaviate, Qdrant - $50-500/month minimum
⚙️
Chunking Overhead
Splitting strategies, overlap logic, metadata management
⏱️
Retrieval Latency
Additional 200-500ms per request for embedding + search
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source code
and
production patterns
at: community.nachiketh.in
Decision Framework
1
Can it fit in the prompt? (<8k tokens)
YES → Use prompt engineering. Stop.
SIMPLE
2
Is the knowledge static?
YES → Consider fine-tuning
SIMPLE
3
Does it change frequently?
YES → NOW you need RAG
RAG
Get
source code
and
production patterns
at: community.nachiketh.in
When to Actually Use RAG
📚
Knowledge base is LARGE (>100k tokens)
Example: Product documentation, legal corpus, research papers
🔄
Information changes FREQUENTLY
Example: News, pricing, inventory, policy updates
🔌
Context must be EXTERNAL to model
Example: User-specific data, company-internal docs, compliance
Get
source code
and
production patterns
at: community.nachiketh.in
START SIMPLE.
ADD COMPLEXITY ONLY WHEN FORCED.
📥
Join Community
community.nachiketh.in
🎓
Learn Production Systems
bootcamp.nachiketh.in
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source code
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production patterns
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