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why RAG Only??
- Anand Nerurkar
- Dec 2
- 8 min read
1️⃣ What is RAG (Retrieval Augmented Generation)?
RAG = Retrieval + LLM Reasoning
Instead of relying only on what the LLM was trained on, we:
Retrieve relevant enterprise-approved documents (policies, procedures, contracts, past cases)
Augment the LLM prompt with this retrieved content
Let the LLM generate a grounded, evidence-based answer
Conceptual Flow
User Question
↓
Retriever (Vector / Search)
↓
Relevant Chunks from Enterprise Knowledge
↓
Prompt = Question + Retrieved Evidence
↓
LLM
↓
Grounded, Auditable Answer
So the LLM does not invent knowledge — it reasons only on what you give it.
2️⃣ Why RAG is Almost Mandatory for BFSI / Regulated Systems
✅ 1. LLM Training Data is NOT Trusted for BFSI
Public LLMs are trained on:
Internet data
Open documents
Unknown sources
BFSI decisions must be based on:
RBI policies
Internal credit policies
Product rules
Signed contracts
👉 RAG ensures the LLM answers only from bank-approved content.
It connects an LLM to your enterprise knowledge at runtime instead of relying only on what the model learned during training.
Core Problems RAG Solves
Business / Technical Problem | How RAG Solves It |
LLM knowledge is static | RAG fetches live data |
Hallucination risk | Grounded responses from verified documents |
No access to internal data | RAG connects to private KBs |
Data keeps changing | Just update the index, no retraining |
Regulatory audit needed | You can trace sources |
Cost of fine-tuning | RAG is much cheaper |
✅ Simply put: RAG = Real-time enterprise brain for LLMs
✅ 2. You Cannot Fine-Tune on Large, Sensitive, Changing Policies
Policies:
Change frequently
Are confidential
Cannot always be pushed to an external model
RAG lets you:
Update knowledge without retraining
Swap documents in seconds
Maintain strict data residency
✅ 3. Regulator & Audit Requirement
With RAG you can say:
“This answer is based on Policy X, Clause 4.2, Version 7, approved on Date Y.”
Without RAG:
You cannot prove where the answer came from.
This fails audit, RBI, SOC2, ISO compliance.
✅ 4. Hallucination Control
LLMs hallucinate when:
They lack context
Or the question is outside training scope
RAG:
Forces grounding
Allows you to enforce:
“Answer only if supported by retrieved evidence”
Otherwise return: INSUFFICIENT EVIDENCE
3️⃣ Why Not Just Use Other Implementations Instead of RAG?
Let’s compare all realistic alternatives:
❌ Option 1: Use LLM Without RAG (Pure Prompting)
Example:
“Explain why this loan was rejected”
LLM has:
No access to your policies
No access to your customer data
No access to your credit rules
Problems:
❌ Hallucinations
❌ No audit trail
❌ No regulatory acceptance
❌ Wrong legal interpretation
✅ Good only for:
Grammar fixes
Generic explanations
Marketing copy
🚫 Not acceptable for BFSI decision support.
⚠️ Option 2: Fine-Tune the LLM Instead of RAG
You train the model on:
Internal policies
SOPs
Past loan cases
Problems:
❌ Extremely costly
❌ Slow to update
❌ Risky from data-leak perspective
❌ Hard to prove which clause influenced which answer
❌ Still no guaranteed factual grounding
✅ Fine-tuning is good for:
Tone
Domain language
Style adaptation
🚫 Fine-tuning cannot replace RAG for dynamic regulated knowledge.
❌ Option 3: Traditional Rules Engine Only
You hard-code:
Credit policies
Risk thresholds
Decision rules
Problems:
❌ Cannot explain complex reasoning in natural language
❌ Cannot summarize multi-document evidence
❌ Cannot assist underwriters in investigation
❌ Poor for edge-case reasoning
✅ Still required for:
Deterministic approvals
Regulatory hard-stops
✅ But rules + RAG = perfect comboRules decide → RAG explains why.
❌ Option 4: Use Search + UI Without LLM
Underwriter manually:
Searches policies
Reads documents
Interprets them
Problems:
❌ Slow
❌ Error-prone
❌ High operational cost
❌ No consistency
RAG replaces:
Manual search
Manual correlation
Manual summarization
Other Perspective
===
Other Implementations Instead of RAG
You are absolutely right — RAG is not the only approach. Here are the main alternatives:
1. Prompt Stuffing (Context Injection)
You manually pass large text inside every prompt.
Example:
System: Here is the full policy document...
User: Answer based on above
✅ Simple❌ Token limit issues❌ Expensive❌ Not scalable❌ No governance❌ Very slow for large docs
👉 Used only for small static content
2. Fine-Tuning the LLM
You retrain the model using domain data.
✅ Good for style, tone, classification✅ Fast inference❌ Very expensive❌ Data becomes outdated❌ No source citation❌ Risky for compliance❌ Cannot update in real time
👉 Used for:
Fraud detection classification
Sentiment analysis
Document type classification
👉 NOT ideal for knowledge retrieval
3. Tool Calling / Agent Search (Without RAG)
The LLM calls APIs, DB queries, or web search.
✅ Great for real-time transactional data✅ Deterministic✅ Auditable❌ Not suitable for unstructured documents❌ Complex orchestration❌ Still needs a knowledge index
👉 Example:
Fetch loan status
Get balance
Pull transaction history
4. Full-Text Search (Elastic / SQL LIKE)
You use keyword search instead of embeddings.
✅ Works for exact matches✅ Cheap❌ Fails on semantic questions❌ Poor recall❌ No reasoning over content
👉 Example:
“Find policy clause 14.2”
5. Knowledge Graph + LLM (GraphRAG)
Data stored as entities & relationships.
✅ Powerful reasoning✅ Excellent for fraud, recommendations✅ High explainability❌ Very expensive to build❌ Requires data modeling❌ Slow to scale enterprise-wide
👉 Used in:
AML
Fraud networks
Supply chain optimization
4️⃣ Why RAG is the Correct Enterprise Pattern
Requirement | Pure LLM | Fine-Tuning | Rules Only | RAG ✅ |
Uses internal policy | ❌ | ✅ | ✅ | ✅ |
Real-time updates | ❌ | ❌ | ✅ | ✅ |
Explainable | ❌ | ⚠️ | ✅ | ✅ |
Auditable | ❌ | ❌ | ✅ | ✅ |
Low hallucination | ❌ | ⚠️ | ✅ | ✅ |
Works with GenAI reasoning | ❌ | ✅ | ❌ | ✅ |
Regulator safe | ❌ | ⚠️ | ✅ | ✅ |
✅ RAG is the only approach that satisfies all BFSI constraints while still getting GenAI benefits.
5️⃣ Your Interview-Ready One-Liner
You can confidently say this:
“We used RAG because in BFSI we cannot allow GenAI to rely on public training data or probabilistic memory. RAG lets us ground every GenAI response on bank-approved policies and real case data, gives us versioned auditability, prevents hallucinations, and allows instant knowledge updates without retraining models. Fine-tuning is used only for language adaptation, while RAG is used for factual authority.”
6️⃣ Where You Use RAG in Your Lending Platform
✅ Underwriter Copilot (risk explanation)
✅ Borrower Copilot (loan agreement explanation)
✅ Operations Copilot (missing documents, SLA rules)
✅ Compliance Copilot (RBI rule interpretation)
✅ Audit & Investigation tools
7️⃣ Summary
“RAG is not a GenAI feature; it is an enterprise control plane for GenAI. It sits between untrusted LLMs and regulated business knowledge and enforces governance, explainability, and factual grounding at scale.”
Why RAG Is the Most Practical Enterprise Choice
RAG sits perfectly between cost, scalability, governance, and accuracy.
Capability | RAG | Fine-Tune | Prompt Stuffing | Tool API |
Uses private data | ✅ | ✅ | ✅ | ✅ |
Real-time updates | ✅ | ❌ | ❌ | ✅ |
Cost efficient | ✅ | ❌ | ❌ | ✅ |
Source traceability | ✅ | ❌ | ❌ | ✅ |
Hallucination control | ✅ | ❌ | ❌ | ✅ |
Handles PDFs, policies | ✅ | ❌ | ✅ | ❌ |
Governance ready | ✅ | ❌ | ❌ | ✅ |
Scales to TBs of data | ✅ | ❌ | ❌ | ❌ |
👉 This is why 90% of enterprise GenAI apps use RAG as the backbone.
4️⃣ Trade-Offs of RAG (Very Important for Interviews)
RAG is powerful but NOT free of problems:
❌ Limitations of RAG
Latency
Embedding + search + LLM = response slower than pure LLM
Retrieval Risk
If wrong chunks are retrieved → wrong answer
Chunking Errors
Bad chunking = broken context
Vector Drift
When documents change but embeddings are stale
Security
Improper filtering can leak confidential data across roles
Operational Overhead
Needs:
Ingestion pipeline
Re-indexing
Monitoring
Drift detection
5️⃣ When You Should NOT Use RAG
You should avoid RAG when:
Scenario | Better Choice |
Pure classification | Fine-tuning |
Real-time DB answers | Tool calling |
Small static FAQ | Prompt stuffing |
Relationship-heavy reasoning | Knowledge Graph |
Extremely low latency systems | Fine-tuned small model |
✅ A strong architect never says “RAG everywhere” — they choose contextually.
6️⃣ Correct Enterprise Strategy (Best Practice)
In real BFSI production systems, we use Hybrid Architecture:
User
↓
LLM Gateway
↓
Decision Layer
├── Tool Call (for live data)
├── RAG (for policy & documents)
├── Fine-Tuned Model (for classification)
└── Knowledge Graph (for fraud / AML)
This gives:
✅ Accuracy
✅ Compliance
✅ Performance
✅ Cost control
7️⃣ One-Liner You Can Use in Interviews
“RAG is primarily used because it gives LLMs real-time, governed, auditable access to enterprise knowledge without retraining, but it must be combined with tool calling, fine-tuning, and knowledge graphs depending on latency, accuracy, and compliance needs.”
8️⃣ BFSI Example to Show Maturity
Loan Policy Assistant
Approach | Risk |
Fine-tuned only | Outdated RBI rules |
Prompt stuffing | Token overflow |
Tool-only | Cannot interpret PDFs |
✅ RAG | Live RBI + internal policy + audit logs |
1️⃣ Whiteboard Explanation (2–3 Minutes, Executive-Friendly)
Step 1 — Start With the Problem
“LLMs are powerful but they suffer from three enterprise blockers: Outdated knowledge Hallucination No access to private data”
Draw this:
LLM (Static Knowledge @ Training Time)
❌ No RBI updates
❌ No internal policy
❌ Hallucinates
Step 2 — Introduce RAG as a Bridge
“RAG connects the LLM to live enterprise knowledge at runtime.”
User Question
↓
Retriever (Vector Search)
↓
Enterprise Documents (Policies, RBI, SOPs)
↓
LLM (Grounded Answer)
Key line:
“RAG does not retrain the model, it injects truth at inference time.”
Step 3 — Explain Why Not Only RAG
“RAG alone is insufficient for transactional systems.”
You draw:
Decision Layer
├── RAG → policies, manuals
├── Tool Calling → live DB, balance, KYC
├── Fine-Tuned Model → classification
└── Knowledge Graph → fraud & AML
Final whiteboard closer:
“RAG is the knowledge brain, tools are the action hands, fine-tuning is the reflex, and graphs are the reasoning network.”
2️⃣ Spring AI / Enterprise Text Architecture Diagram
This is exactly how you explain it in system design rounds:
[ User / Agent UI ]
|
v
[ API Gateway + Auth (OAuth2, Keycloak, AAD) ]
|
v
[ LLM Orchestrator / Spring AI ]
|
v
[ Decision Router ]
| | | |
| | | |
Tool RAG Fine-Tuned GraphRAG
Call Flow Model Engine
| | | |
| | | |
Core Vector ML Inference Neo4j /
APIs DB Endpoint TigerGraph
(CBS, (Pinecone, (Fraud CLF) AML Network)
LMS, Weaviate)
CRM)
------------------- RAG PIPELINE -------------------
[ Document Ingestion ]
↓
[ OCR / Parsing ]
↓
[ Chunking (Semantic) ]
↓
[ Embedding (OpenAI / bge / e5) ]
↓
[ Vector Store ]
↓
[ Hybrid Retrieval (BM25 + Vector) ]
↓
[ Re-Ranker (Cross Encoder) ]
↓
[ Top-K Context to LLM ]
Security & Governance (you say verbally):
Role-based retrieval filters
PII redaction
Prompt logging
Source citation
Audit trail
3️⃣ Real Production Failure Case (BFSI) + How It Was Fixed
❌ Failure Case: Wrong Loan Foreclosure Fee Advice
Incident:
Customer asked:“What is the foreclosure charge on my home loan?”
RAG retrieved old policy PDF
LLM responded: 2% penalty
Actual updated policy: 0% (RBI waiver)
Bank had to compensate customer
🔍 Root Cause Analysis
Layer | Issue |
Ingestion | Updated policy not re-indexed |
Retrieval | Old chunk ranked higher |
Validation | No recency filter |
Governance | No human-in-loop for financial answers |
✅ Fix Implemented (Production Grade)
1. Time-aware metadata filtering:
filter = policy_date > last_30_days
2. Dual-source validation:
Internal Policy + RBI Master Circular
3. Confidence threshold:
If similarity < 0.75 → escalate to human
4. Auto re-index job:
Every 6 hours on document repo change
5. Regulated Response Mode:
“This is as per RBI circular dated DD-MM”
✅ Final Result
Zero regulatory complaints
42% call-center deflection
100% auditable answers
4️⃣ Enterprise Decision Matrix (Put Directly in Your PPT)
Use Case | Best Tech | Why |
Policy Q&A | ✅ RAG | Dynamic, auditable |
Loan Status | ✅ Tool Calling | Real-time DB |
Fraud Detection | ✅ Knowledge Graph + ML | Network reasoning |
Sentiment Analysis | ✅ Fine-Tuned Model | High accuracy |
Static FAQ Bot | ✅ Prompt Stuffing | Cheap & simple |
AML Network | ✅ GraphRAG | Entity relationships |
Credit Classification | ✅ Fine-Tune | Deterministic |
Compliance Assistant | ✅ RAG + Human Review | Regulatory safe |
Customer Chat | ✅ RAG + Tool Hybrid | Knowledge + Action |
5️⃣ Trade-Off Summary Table (Architect Level)
Dimension | RAG | Fine-Tuning | Tool Calling | Prompt Stuff |
Knowledge freshness | ✅ Live | ❌ Static | ✅ Live | ❌ Static |
Cost | ✅ Medium | ❌ High | ✅ Low | ❌ High |
Governance | ✅ Strong | ❌ Weak | ✅ Strong | ❌ Weak |
Latency | ❌ Medium | ✅ Fast | ✅ Fast | ❌ Slow |
Hallucination control | ✅ Strong | ❌ Weak | ✅ Strong | ❌ Weak |
Enterprise scale | ✅ High | ❌ Low | ✅ High | ❌ Low |
6️⃣
“We do not choose RAG because it is fashionable; we choose it because it is the only economically viable way to give LLMs governed, live, auditable access to enterprise knowledge. However, in production we always deploy RAG inside a hybrid AI architecture with tool calling, fine-tuned models, and knowledge graphs based on the latency, risk, and compliance profile of each use case.”
7️⃣ Bonus: Interview Trick Question & Smart Answer
Q: Why not just fine-tune on RBI circulars instead of RAG?
“Because RBI circulars change monthly. Fine-tuning would require repeated retraining, re-certification, and re-approval, whereas RAG allows us to update knowledge with zero model retraining and full audit traceability.”
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