Enterprise Semantic Search

RAG Engineer & Consultant in India for Source-Cited AI Assistants

I design and build retrieval-augmented generation systems that answer from your documents, content, databases, or knowledge base with source citations, retrieval controls, evals, logs, and production-ready APIs.

Production RAG Requirements Checklist

Document Ingestion Pipelines
Smart Chunking Strategy
Metadata Enrichment Strategy
Embedding Model Selection
Vector Database Schema Design
Hybrid Dense/Sparse Search
Cross-Encoder Reranking
Grounding Context Prompts
Verifiable Source Citations
Graceful Refusal Behavior
Continuous Evaluation Datasets
p95 Latency Tracking
Downstream Cost Tracking
User Thumbs Up/Down Feedback
Admin Review & Logs Tools
Observability & Active Alerting

What a Good RAG System Needs

Context-Aware Chunking

Document splits based on semantic sections (headers, tables, paragraphs) rather than rigid character counts.

Hybrid Dense/Sparse Search

Combining neural semantic search (vector) with exact keyword matching (BM25) to cover synonyms and serial numbers.

Cross-Encoder Reranking

Using lightweight reranker models (like Cohere or BGE) to ensure the absolute most relevant chunks are fed into the context window.

Citation Telemetry

Tracing every generated claim to its source chunk index, allowing users to verify facts in one click.

Common RAG Failure Modes

Lost in the Middle

LLMs ignore critical facts hidden in the middle of extremely large context windows, causing wrong answers.

Source Hallucination

Making up source citations or linking to completely unrelated document chunks when retrieval fails.

Data Leakage

Exposing private or sensitive files across organization roles due to a lack of strict metadata tenant filters.

Silent Retrieval Failure

Generating highly confident answers based on completely outdated or empty retrieved contexts.

My RAG Implementation Process

Telemetry & Schema Design

Audit your data schemas, design ingestion ETL pipelines, and model metadata filtering policies.

Vector Store Tuning

Select the optimal embedding model, setup vector index settings (Pinecone, Qdrant, pgvector), and tune hybrid search weights.

Reranker & Prompt Assembly

Integrate multi-stage reranking pipelines, enforce rigid system prompts, and build robust refusal and citation templates.

Evals & Analytics Handoff

Set up evaluation datasets (RAGAS framework), instrument downstream GA4/BigQuery telemetry, and deploy admin review tools.

RAG System Architecture Options

Lightweight SQL RAG

Postgres (pgvector) + FastAPI

SaaS products with highly transactional data, strict role policies, and lower volumes (<50k pages).

Dedicated Vector Store RAG

Pinecone / Qdrant + FastAPI + Celery

High-volume, sub-second search applications needing advanced metadata isolation and scaling.

Hybrid Neural RAG

BM25 + Vector + Cohere Rerank

Enterprise search portals requiring high lexical precision (IDs, skus) and semantic depth.

1. Evaluation & Hallucination

I establish strict test suites using frameworks like RAGAS to score faithfulness, answer relevance, and context recall, checking prompts against versioned retrieval indexes before shipping.

2. Citation & Source Display

Citations are designed at the ingestion level. I inject chunk metadata filters that return precise page numbers, section headers, and click-through link tags in streaming markdown formats.

3. Analytics & Feedback

Every prompt, retrieval duration, embedding token cost, and thumbs-up/down click is cleanly instrumented and routed into BigQuery to constantly refine retrieval weights and chunk strategies.

Grounded RAG Project Proof

Explore real, verified case studies where I designed and shipped advanced semantic search pipelines.

Retrieval-Augmented Generation

Technical Blog RAG Assistant

Designed a precise, high-accuracy Q&A search system backed by vector search pipelines, custom semantic chunking schemas, and multi-stage prompt validation rigs.

View RAG study

AI Marketing Audit Platform

Adticks

Engineered custom parallel indexing models and multi-stage NLP analysis layers capable of running automated SEO/AEO/GEO diagnostic audits across 10,000+ pages simultaneously.

View Adticks study

FastAPI browser-workflow automation case study

Udemy Enroller

Built a private FastAPI automation project exploring async task queues, Playwright workflow orchestration, bounded worker concurrency, secure session-state handling, and telemetry logging.

View automation study

Frequently Asked Questions

Clear answers about development processes, model capabilities, and implementation scope.

How do you reduce hallucination risk in a RAG assistant?

No LLM is 100% immune, but I achieve a reduced hallucination risk through retrieval boundaries and evals by: (1) enforcing strict prompt boundaries that forbid answering without context, (2) using citation parser schemas, and (3) adding evaluation guardrails.

Can you implement RAG on private local data?

Yes. I build secure vector stores utilizing strict metadata tenant filtering or private VPC deployments to ensure user permissions are respected and private files are never leaked.

What embedding models and vector databases do you recommend?

I recommend Cohere or OpenAI text-embeddings-3-large for general use, and Pinecone or Qdrant for dedicated vector indexing. If your stack is Postgres, pgvector is an exceptionally clean, low-overhead starting point.

How do you trace costs and latencies?

I integrate downstream observability layers (such as LangSmith or custom logging) to trace exact prompt/completion tokens, model latencies, and routing flows, giving you full visibility into run costs.

Let's build a high-precision RAG system

Schedule a technical discovery call to review your current document formats, discuss chunking and vector storage designs, and outline your evaluation goals.

Schedule RAG discovery call

About Madhu Dadi (Profile)|Verified Credentials & Proof