RAG Pipeline Development
RAG Pipelines That Ground Your LLM in Real Data
Your LLM does not know your documents, products, or internal knowledge. RAG fixes that. Kovil AI builds production retrieval-augmented generation pipelines that cut hallucinations, ground every response in your data, and include RAGAS evaluation from day one.
What is RAG?
How Retrieval-Augmented Generation Works
RAG connects a large language model to a retrieval system over your own data. At query time, the system retrieves the most relevant chunks from your knowledge base and provides them as context to the LLM. The model then generates a response grounded in that specific retrieved information rather than its general training data.
The result is AI that knows your documents, policies, and products, and can answer questions about them accurately. Every answer traces back to a source. Hallucinations drop sharply because the model works from retrieved facts, not from memory.
Kovil AI's approach
We build the RAGAS evaluation pipeline before the RAG system itself. Quality must be measurable from the first iteration. Every production RAG pipeline includes automated faithfulness, relevancy, and context recall scoring baked into the CI pipeline.
60-80%
Reduction in LLM hallucination rate with properly built RAG vs no grounding (Stanford AI Lab)
$4.4B
Enterprise RAG market size projected by 2027 (Grand View Research)
3x
Higher answer accuracy when RAG includes mandatory source citation enforcement
Pipeline Architecture
The Six Layers of a Production RAG Pipeline
Most RAG demos implement only retrieval and generation. Production systems require all six layers, each engineered carefully.
Document Ingestion
Parsing PDFs, Word docs, HTML, databases, and APIs into clean text. Handling tables, images, and mixed-format documents properly.
Intelligent Chunking
Splitting documents into chunks that preserve semantic meaning. Wrong chunk sizes destroy retrieval quality. We tune this per document type.
Embedding and Indexing
Converting chunks to vector embeddings using the right model for your domain, then indexing with proper metadata into the vector store.
Retrieval and Re-ranking
Semantic search retrieves candidates. A cross-encoder re-ranker re-orders by relevancy before passing context to the LLM.
LLM Generation with Citation
The LLM generates answers grounded in retrieved context, with mandatory source citations so every claim is traceable.
RAGAS Evaluation Pipeline
Automated evaluation measuring faithfulness, answer relevancy, and context recall on every deployment and regression.
Vector Database Selection
Which Vector Database Is Right for Your RAG System?
Database selection impacts retrieval quality, latency, and operational overhead significantly. Here is how the leading options compare.
| Database | Scalability | Latency | Hybrid Search | Managed | Best For |
|---|---|---|---|---|---|
| Pinecone | Very High | Very Low | Good | Fully Managed | Production at scale, fast time-to-value |
| Weaviate | High | Low | Excellent | Managed + Self-host | Hybrid semantic and keyword search |
| Qdrant | High | Very Low | Excellent | Managed + Self-host | High-performance custom deployments |
| pgvector | Medium | Medium | Full SQL filters | Self-host | Teams already on PostgreSQL |
| Chroma | Low-Medium | Fast (local) | Basic | Self-host only | Development and prototyping |
Stop Hallucinating
Ground your LLM in your own data in 3 to 6 weeks.
RAGAS evaluation included. Fixed-price. No delivery risk.
Supported Data Sources
We Build RAG Over Any Data Source
Most enterprise RAG projects need to unify multiple data sources. We design retrieval architectures that handle heterogeneous data cleanly.
PDF Documents
Contracts, research papers, manuals, reports, including tables and multi-column layouts
Word and Office Files
DOCX, PPTX, XLSX with proper structure extraction and table handling
Web and HTML Content
Internal wikis, product documentation sites, support knowledge bases
Confluence and Notion
Workspace documentation with page hierarchy and metadata preserved
SQL Databases
Text-to-SQL for structured data with schema-aware query generation
Code Repositories
GitHub, GitLab code search and documentation with AST-aware chunking
Case Study — Legal / LegalTech
RAG over 15 Years of Contract Precedents for a 60-Attorney Law Firm
94%
clause analysis automated
78%
faster review time
$380K
partner hours reclaimed annually
FAQs
Common Questions About RAG Pipeline Development
What is RAG (Retrieval-Augmented Generation)?
RAG is an architecture that combines a retrieval system with a large language model. Instead of relying solely on the LLM's training data, RAG retrieves relevant documents from your own knowledge base at query time and provides them as context. The result is responses grounded in your specific information with dramatically lower hallucination rates.
Why does my LLM application need RAG?
Without RAG, LLMs answer from their training data, which is outdated, generic, and knows nothing about your company or products. RAG grounds every response in your knowledge base, making the AI accurate, current, and proprietary to your business.
Which vector database do you recommend?
It depends on your requirements. Pinecone is our default for teams that want a fully managed solution and need to move fast. Weaviate is excellent for hybrid semantic and keyword search. pgvector is the right call when you are already on PostgreSQL and want minimal infrastructure complexity.
How do you reduce hallucinations in a RAG system?
Grounding responses in retrieved context is the primary mechanism. We also add citation enforcement (the LLM must cite its source chunk), answer relevancy checks with RAGAS, document re-ranking before generation, and a fallback behavior that returns "I don't know" when retrieval quality is too low.
What types of data can you build RAG pipelines over?
PDFs, Word documents, HTML, Confluence wikis, Notion databases, SQL databases, CSV files, code repositories, and Slack histories. Most enterprise RAG projects require unifying multiple heterogeneous data sources into a single retrieval layer.
How do you evaluate whether a RAG system is working well?
We use RAGAS to measure faithfulness (is the answer grounded in the retrieved context?), answer relevancy (does it address the question?), and context recall (did retrieval find the right chunks?). We build the evaluation pipeline before the RAG system so quality is measurable from iteration one.
How long does it take to build a production RAG pipeline?
A single-source RAG system can be built and deployed in 3 to 6 weeks. Multi-source RAG with hybrid search, re-ranking, and custom evaluation typically takes 6 to 10 weeks.
Can RAG work with structured data like databases or spreadsheets?
Yes. For structured data we use text-to-SQL for database querying, metadata filtering on structured fields, and hybrid pipelines that combine structured lookups with semantic search over unstructured text.
Your Knowledge Base. Your LLM. Zero Hallucinations.
Fixed-price RAG pipeline delivery with RAGAS evaluation built in from day one.
Book a Free RAG Architecture Call