RAG/

1. RAG PIPELINES — Enterprise-Grade Reference Architecture

RAG in enterprise = 4 layers

1. Ingestion & Preprocessing

2. Indexing & Storage

3. Retrieval & Ranking

4. Generation & Guardrails

1.1 RAG Pipeline — End-to-End Architecture

A. Document Ingestion Layer

• OCR (AWS Textract / Azure Form Recognizer / Tesseract)

• PII masking (Rule-based + ML-based)

• Document classification (SVM/BERT/LLMs)

• Chunking (semantic-aware: sentences, headings)

• Normalization (clean, dedupe, flatten PDFs)

B. Embedding + Indexing Layer

• Embedding model selection:

  • Instruction-based embeddings (OpenAI text-embedding-3-large)

  • Domain fine-tuned embeddings (finance, AML, onboarding)

• Metadata:

  • doc_id

  • version

  • policy_type

  • validity_date

  • regulatory_flag

• Vector DB choices:

  • Postgres + pgvector (regulated BFSI)

  • Pinecone

  • Weaviate

  • Milvus

C. Retrieval Layer

• Hybrid Retrieval

  • Vector search

  • BM25

  • Dense + Sparse Fusion

• Re-ranking

  • Cross-encoder (e.g., bge-reranker)

  • LLM re-ranker (costly but accurate)

• Retrieval Filters

  • Recency filter (updated policy only)

  • Version filter

  • Tenant filter (ICICI vs HDFC)

D. Generation Layer

• Response synthesis

• Policy-grounded LLM

• Answer reliability scoring

• Hallucination detection

  • Proximity score threshold

  • Coverage test

  • Answer consistency check

🟩 1.2 RAG Key Design Principles

1. Vector Consistency

• Invalidate & rebuild embeddings when policy version changes

• Maintain index freshness SLA (e.g., 5 minutes after update)

2. Retrieval Safety

• “Grounded Only Mode” →If retrieval returns < similarity threshold →

  “Answer not found in policy.”

3. Observability

• Log: retrieval candidates, final chunks, hallucination score

2. MULTI-AGENT WORKFLOWS — Enterprise Architecture Blueprint

AI Systems are shifting from one large model → multiple small agents, each with a role.

2.1 Multi-Agent Types

1. Orchestrator Agent

• Top-level planner

• Breaks tasks into sub-tasks

• Decides which agent handles each step

• Ensures compliance + governance

2. Specialist Agents

• Domain expert agent (e.g., Lending Policy Agent)

• KYC agent

• Risk decision agent

• Fraud scoring agent

• Tech architecture agent

• SQL/data extraction agent

• Code generation agent

3. Tool Agents

• OCR agent

• Vector DB agent

• Search agent

• API caller agent

• ETL/data prep agent

4. Guardrail & Safety Agents

• Policy compliance checker

• PII auditor

• Hallucination detector

• Version consistency checker

2.2 Multi-Agent Workflow – Example (Digital Lending)

User: "Tell me whether this candidate is eligible for loan."

Step-by-step Flow

1. Orchestrator Agent

   • Detects need for: OCR, vector DB retrieval, risk scoring

   • Creates workflow plan

2. OCR Agent

   • Extracts text from KYC PDF

3. Data Extraction Agent

   • Extracts name, PAN, salary, employment type

4. Policy Retrieval Agent (RAG)

   • Retrieves lending criteria from vector DB

5. Credit Score Agent

   • Calls score service

6. Risk Decision Agent

   • Combines OCR + data + rules + policy + risk models

7. Compliance Agent

   • Ensures decision is policy-grounded

8. Response Generator Agent

   • Produces the final explanation

2.3 Multi-Agent Patterns

A. ReAct (Reason → Act → Observe → Refine)

Use when tasks need iterative reasoning.

B. Hierarchical Agents

One “boss”, many “workers”.

C. Swarm (Autonomous Collaboration)

Agents message each other to refine outputs.

D. Toolformer Pattern

LLM chooses tools dynamically.

2.4 Multi-Agent Guardrails

• Task deduplication

• Loop detection

• Maximum depth per agent

• Cross-agent memory

• Structured communication (“thought”, “action”, “observation”)

• Hallucination scoring per agent

  
  

3. MODEL EVALUATION HARNESS — LLMOps Architecture

A Model Evaluation Harness ensures your models are:✔ reliable✔ accurate✔ grounded✔ safe✔ robust

This is mandatory for BFSI, lending, onboarding, fraud.

3.1 Types of Evaluation

1. Functional Evaluation

• Correctness

• Completeness

• Clarity

2. Groundedness Evaluation

• Based only on retrieved context

• Compute:

  • Faithfulness

  • Relevance

  • Coverage

3. Safety Evaluation

• Bias testing

• PII protection

• Regulatory compliance

4. Adversarial / Red Team Testing

• Injection attacks

• Prompt jailbreaks

• Policy override attempts

• Refusal testing

5. Latency Evaluation

• Time to retrieval

• Time to first token

• End-to-end latency

3.2 Evaluation Harness Architecture

User Query 
   │
   ▼
LLM Pipeline Under Test
   │
   ├──> Capture Retrieval Chunks
   ├──> Capture Model Output
   └──> Capture Thought/Reasoning (hidden)
   │
   ▼
Evaluation Runner
   │
   ├── Functional Tests
   ├── Groundedness Tests
   ├── Safety Tests
   ├── Red Team Tests
   └── Regression Tests
   │
   ▼
Metrics & Dashboard

3.3 Evaluation Metrics (Enterprise)

Functional

• Answer correctness

• Completeness score

• Answer length deviation

Groundedness

• Chunk coverage (%)

• Faithfulness score

• Retrieval relevance

Safety

• PII leakage

• Offensive content

• Regulatory-compliance score

Red Team

• Jailbreak resistance

• Prompt-injection susceptibility

Performance

• TTFT

• Tokens used

• Cost per query

3.4 Harness Outputs

• Pass/Fail summary

• Detailed failure cases

• Explainability report

• Policy grounding heat-map

• Regression drift chart

• Model version comparison

3.5 When to Run Evaluation Harness

• Before deployment

• Before policy change

• After embedding refresh

• Daily scheduled run

• Before customer demo

• Before releasing new agent

“We built a context-integrity microservice to solve three enterprise problems with LLMs: token explosion, context drift, and untrusted retrievals. The service stores a canonical session state (session_id, step_id, policy & embedding versions, active chunk pointers) in a lightweight hot store (Redis) with durable snapshots in Postgres. We roll older conversation into compact rolling summaries using a summarizer worker so the model gets only the essential state plus the last 3–5 messages.

For RAG we enforce metadata filters (tenant, policy_version, embedding_version) and a minimum similarity threshold so the model can only base answers on verified chunks. We detect semantic drift by comparing prompt embeddings with the last-context embedding — if similarity falls below 0.65 we rehydrate state and re-run retrievals.

To prevent loops and duplicate work we use idempotent task keys and strict tool response schemas; automatic retries are limited to a single retry flagged by the tool. Finally, a Model Evaluation Harness captures retrieval candidates and model outputs for functional, groundedness, safety, and adversarial testing, enabling regression detection and compliance reporting. This design achieves robust, auditable, and low-cost LLM operations for regulated financial workflows.”

✅ What type of chunking is best? (Short Answer)

Hybrid Semantic + Recursive chunking is currently the most reliable and production-proven approach for 95% of enterprise RAG workloads.

But the best chunking depends on:

• Document type (policies, contracts, logs, code, emails)

• Downstream task (search vs QA vs reasoning vs extraction)

• LLM size/window

• Retrieval architecture (RAG vs RAG-Fusion vs ColBERT)

✅ Top 7 Chunking Strategies (w/ When to Use Each)

1. Fixed-size chunking (e.g., 500–1000 tokens)

How it works: break text by token count.Pros: simple, stable performance, baseline.Cons: may split semantic units; needs overlap.

Use when:

• Logs, emails, transcripts

• High-volume ingestion

• Simpler RAG Q&A

• Perfect for fallback chunker

2. Semantic / Embedding-based chunking

How it works: split text based on semantic boundaries (embedding similarity drops).Pros: preserves meaning; fewer hallucinations.Cons: compute-heavy on ingestion.

Use when:

• Policies, legal docs, contracts, standards

• Banking circulars, RBI guidelines

• Documents with irregular structure

• Knowledge retrieval with high accuracy requirements

3. Recursive Hierarchical Chunking (RHC) — recommended default

How it works:

1. Split by large structural boundaries (H1, H2, sections)

2. If too large, split by paragraphs

3. If still large, split by sentences

4. Only last fallback: fixed tokens

Pros:

• Follows document structure

• High answer accuracy

• Low hallucination rates

• Best for long PDF/policies

Use when:

• PDFs with hierarchy

• Long-form documents (policies, manuals, SOPs)

• Multi-agent RAG workflows

• Banking/insurance policy ingestion

This is the industry standard (OpenAI Cookbook, LangChain, LlamaIndex).

4. Sliding Window / Overlap Chunking (20–30% overlap)

How it works: each chunk overlaps with previous/next.Pros: preserves cross-sentence context, improves QA grounding.Cons: more storage + compute.

Use when:

• High-stakes QA (compliance, legal, contracts)

• When answers depend on contextual flow

• Multi-sentence reasoning tasks

5. Semantic Graph Chunking (advanced)

How it works: create nodes from paragraphs; edges based on semantic coherence.Pros: amazing for cross-referencing, multi-hop QA.Cons: expensive, complex.

Use when:

• Multi-hop reasoning

• Large knowledge graphs

• Enterprise search at scale

Used by Google’s RETRO and GraphRAG.

6. Layout-aware Chunking (for PDFs, forms, tables)

How it works: preserves spatial structure (x/y coordinates) using OCR metadata.Pros: best for complex PDFs.Cons: requires OCR toolchain.

Use when:

• Bank statements

• Insurance forms

• Invoices

• PDF with tables and footnotes

In GenAI Production: must use for forms.

7. Code-aware chunking

How it works: split at logical boundaries (classes, functions, imports).Use when:

• Code assistants

• Internal engineering knowledge-bases

✅ What Are Embeddings?

Embeddings are numerical representations of text, images, documents, or objects that capture their meaning, context, and relationships — encoded as high-dimensional vectors.

Example:“Loan eligibility” → [0.234, -0.554, 0.192, ...] (1536-D vector)

Two concepts that “mean similar things” end up near each other in vector space.

📌 Why Enterprises Use Embeddings (Simple → Deep)

⭐ 1. Semantic Search (RAG)

Instead of keyword search, embeddings let you search by meaning.

Query:“maximum LTV allowed for salaried customers”

Vector search retrieves the correct policy rule even if exact words differ.

Used in:

• Lending policies

• KYC rules

• RBI circulars

• SOPs

• Operational checklists

• MF/Insurance compliance

⭐ 2. Document Understanding at Scale

Embeddings let enterprises convert large PDFs, emails, contracts, KYC docs into searchable numeric vectors.

Works across:

• Policies

• SOPs

• Process documents

• Product guidelines

• Risk frameworks

• Training materials

⭐ 3. Multi-Agent Systems Need Embeddings to Share Knowledge

Agents use embedding-based retrieval to store and fetch:

• decisions

• constraints

• conversation context

• memory states

• policies

• customer profiles

Without embeddings → agents forget context or hallucinate.

⭐ 4. Grounding LLMs → Reduce Hallucination by 60–80%

LLMs hallucinate because they rely on general training knowledge.Enterprises want factual answers based on private documents (policies, rules).

Embeddings let you:

1. Store your documents in vector DB

2. Retrieve the exact chunks relevant to the question

3. Feed back into LLM

4. Get grounded, policy-correct output

This is the core of RAG (Retrieval-Augmented Generation).

⭐ 5. Matching, Classification & Clustering

Embeddings allow systems to identify:

• Similar customers

• Similar claims

• Similar credit behaviors

• Similar transactions (fraud)

• Similar disputes

• Duplicate documents

• Similar complaints

This reduces operational workload by 40–60%.

⭐ 6. Risk Analytics & Fraud Detection

Embedding-based ML detects patterns better than rule-based systems.

Examples:

• Similar fraud patterns across accounts

• Similar unusual income flows

• Similar document tampering signals

Embeddings allow you to detect latent risk, not just explicit rules.

⭐ 7. Personalization & Recommendations

Enterprises use embeddings for:

• Personal finance advice

• Mutual fund recommendations

• Insurance riders

• Fraud dispute actions

• Ticket routing

All done through similarity.

⭐ 8. Cross-Document Reasoning in Lending & KYC

To evaluate a loan, an agent must “connect”:

• KYC identity

• Income stability

• Bank patterns

• Lending policy

• Product rules

• Exceptions

Embeddings allow the system to:

✔ fetch the right policy✔ understand user profile✔ apply relevant rules✔ justify reasoning

📌 Why Are Embeddings Better Than Keyword Search?

Feature	Keyword Search	Embeddings
Understand meaning	❌ no	✅ yes
Handle synonyms	❌ no	✅ yes
Understand context	❌ no	✅ yes
Find related policies	❌ poor	✅ excellent
Multi-language	❌ no	✅ yes
Fuzzy matching	❌ manual	✅ built-in
Cross-document reasoning	❌ difficult	✅ natural

📌 Where Embeddings Fit in Enterprise Architecture

Input → Chunking → Embedding → Vector DB → Retrieval → LLM → Output

Works with:

• Spring AI

• LangChain4j

• Azure AI Search

• Pinecone

• Qdrant

• pgvector

• Weaviate

Embeddings are the foundation of enterprise GenAI.

🔥 Short Executive Summary for Interviews

“Embeddings convert enterprise documents into numeric vectors that capture meaning, not keywords. This enables semantic search, policy reasoning, multi-agent coordination, and factual RAG. It dramatically reduces hallucination, improves accuracy, and allows enterprises to use LLMs safely on private data.”