How is RAG different from fine-tuning an AI model?

Fine-tuning changes the AI model itself by training it on your data, which is expensive and makes updates difficult. RAG keeps the model unchanged and instead gives it access to your documents at query time. This means you can update your knowledge base instantly without retraining. RAG is more cost-effective, more accurate for factual questions, and easier to maintain.

What types of documents can RAG process?

RAG can process virtually any text-based content: PDFs, Word documents, web pages, Confluence wikis, Notion databases, Slack messages, emails, CSV/Excel files, code repositories, and API documentation. I build custom document parsers for each source to extract text, tables, and metadata accurately.

How much does RAG development cost?

A document Q&A system starts from EUR 3,000 and takes 3-4 weeks. An enterprise knowledge platform with multiple data sources and role-based access costs EUR 6,000-10,000. Advanced systems with multi-step reasoning and custom optimization cost EUR 10,000+. Monthly running costs are EUR 50-300 depending on usage volume.

RAG Application Development: AI That Knows Your Data

I build AI systems that search your documents, understand context, and answer questions accurately -- grounded in your actual company data, not AI hallucinations.

01 / OVERVIEW

What Is RAG and Why It Matters for Business

RAG (Retrieval-Augmented Generation) is the architecture behind every AI system that needs to answer questions from specific data. Instead of relying on a language model's general knowledge, RAG first searches your documents to find relevant information, then uses that context to generate precise, factual answers with source citations.

This is how companies like Notion, Slack, and Confluence are adding AI search to their products. The difference: I build custom RAG systems tailored to your specific data, business logic, and security requirements. Your data stays on your infrastructure, and the system is optimized for your exact use case -- not a one-size-fits-all solution.

I am Kirill Strelnikov, a freelance AI integration developer based in Barcelona. I have built RAG systems for customer support knowledge bases, internal documentation search, and product catalogs. My RAG applications achieve 90-95% factual accuracy with source citations on every answer.

02 / USE CASES

RAG Applications I Build

RAG is the right solution whenever your AI needs to work with specific, up-to-date information rather than general knowledge. Here are the three most common applications.

Internal Knowledge Base

AI-powered search across your company wiki, SOPs, HR policies, and technical documentation. Employees ask questions in natural language and get instant, accurate answers with links to the source document.

Customer Support AI

A chatbot trained on your product docs, FAQs, and support history. It answers customer questions accurately, cites specific documentation, and escalates to human agents only when needed. Reduces support tickets by 60-70%.

Document Research Agent

AI that analyzes contracts, research papers, legal documents, or regulatory filings. It can compare documents, extract key clauses, summarize findings, and answer complex multi-document questions with evidence.

RAG works with any text-based data: PDFs, Word docs, Confluence pages, Notion databases, Slack messages, emails, code repos, and API documentation. For conversational AI needs, see my AI chatbot development service.

03 / TECHNOLOGY

RAG Tech Stack

Building a production RAG system requires careful selection of embedding models, vector databases, chunking strategies, and retrieval methods. Here is the stack I use.

Python LangChain LlamaIndex OpenAI Embeddings GPT-4o Claude pgvector Pinecone ChromaDB Django PostgreSQL Redis Celery Docker

Embedding & retrieval: I use OpenAI Ada-3 or Cohere embeddings for semantic search, combined with hybrid retrieval (vector + keyword search) for maximum accuracy. Chunking strategy is tailored to your document types -- technical docs need different chunking than legal contracts.

Vector storage: pgvector for PostgreSQL-native solutions (no extra infrastructure), Pinecone for high-scale production, or ChromaDB for rapid prototyping. The choice depends on your scale, budget, and existing infrastructure.

Generation layer: GPT-4o or Claude for answer generation with custom prompts that enforce citation, format control, and domain-specific reasoning. Every answer includes source references so users can verify the information.

04 / RAG VS ALTERNATIVES

RAG vs Fine-Tuning vs Plain LLM

Understanding when RAG is the right approach saves time and money. Here is how it compares to alternatives:

Plain LLM (ChatGPT/Claude): Answers from general training data only. Cannot access your specific documents. Hallucinates freely when it does not know the answer. Fine for general questions, useless for company-specific queries.
Fine-tuning: Trains the model on your data. Expensive (EUR 5,000-50,000+), slow to update (days per retraining), and still hallucinates. Best for teaching the model a new style or domain vocabulary, not for factual Q&A.
RAG (what I build): Searches your documents at query time and grounds answers in actual data. Updates instantly when you add new documents. 90-95% factual accuracy with citations. Costs EUR 3,000-10,000 and runs for EUR 50-300/month.

Bottom line: If your AI needs to answer questions from specific, changing data, RAG is the right approach. If you need to change how the AI writes or reasons, fine-tuning might help. Most business use cases need RAG.

05 / PROCESS

How I Build Your RAG Application

Data Audit & Strategy

I analyze your document corpus: formats, volume, update frequency, and quality. I identify the best chunking strategy, embedding model, and retrieval approach for your specific data. Deliverable: technical specification with architecture diagram and cost estimate.

Document Pipeline

I build custom parsers for each document type (PDF, Word, HTML, Confluence, etc.). Documents are chunked, cleaned, and embedded into your vector database. Metadata extraction ensures accurate filtering and source attribution.

Retrieval Optimization

I tune the retrieval pipeline for your data: hybrid search (semantic + keyword), re-ranking, query expansion, and context window optimization. This phase turns a basic RAG into a production system with 90%+ accuracy.

Application & UI

I build the user interface -- web app, API endpoint, Slack bot, or widget -- and integrate it with your existing systems. Every answer includes source citations and confidence indicators. Role-based access controls who can see what.

Evaluation & Launch

Systematic testing against a question bank covering your key use cases. I measure retrieval accuracy, answer quality, and latency. Continuous monitoring tracks system performance after launch, with automatic alerts for quality degradation.

06 / CASE STUDIES

RAG Projects I Have Delivered

AI Chatbot for E-commerce with Product Knowledge

Built a RAG-powered chatbot for an online clothing store that answers questions from the product catalog, size guides, shipping policies, and return procedures. The system automated 70% of customer support queries and increased conversion by 35% through personalized product recommendations grounded in actual catalog data.

Django OpenAI API RAG E-commerce

Telegram AI Aggregator with Document Understanding

Created a Telegram bot that unifies multiple AI models with document processing capabilities. Users upload documents and ask questions, receiving answers grounded in the uploaded content. The system processes PDFs, images, and text files with automatic language detection and multilingual responses.

Python Telegram Bot API OpenAI Document Processing

07 / PRICING

RAG Development Pricing

Fixed-price contracts based on complexity. All prices include document pipeline, retrieval optimization, application development, testing, and 30 days of post-launch support.

Document Q&A

€3,000 / from

Single-source knowledge base

Up to 500 documents
One document format
Web chat interface
Source citations
Basic analytics
3-4 weeks delivery
30 days support

Knowledge Platform

€6,000 / from

Multi-source enterprise search

Unlimited documents
Multiple document formats
Hybrid retrieval (vector + keyword)
Role-based access control
API + web interface
Usage analytics dashboard
5-7 weeks delivery
30 days support

Research Agent

€10,000 / from

Advanced multi-step reasoning

Multi-document analysis
Custom reasoning chains
Tool use and actions
Domain-specific optimization
Continuous learning pipeline
8-12 weeks delivery
60 days support

Monthly running costs: EUR 50-300 depending on document volume and query frequency. Includes LLM API fees, vector database hosting, and compute.

Frequently Asked Questions

What is RAG and how does it work?

RAG (Retrieval-Augmented Generation) combines a search system with a language model. When someone asks a question, the system first searches your documents for relevant passages, then sends those passages to GPT-4 or Claude along with the question. The AI generates an answer based only on the retrieved context, not its general training. This means answers are grounded in your actual data with source citations.

How is RAG different from fine-tuning?

Fine-tuning permanently changes the AI model by training it on your data -- it is expensive (EUR 5,000-50,000+), slow to update, and still hallucinates. RAG keeps the model unchanged and searches your documents at query time. You can update your knowledge base instantly by adding or removing documents. RAG is 3-10x cheaper, more accurate for factual questions, and easier to maintain.

What document types can your RAG system process?

PDFs, Word documents, HTML pages, Confluence wikis, Notion databases, Google Docs, Slack messages, emails, CSV/Excel files, Markdown, code repositories, and API documentation. I build custom document parsers for each source that handle text extraction, table parsing, image OCR, and metadata preservation.

How accurate are RAG systems compared to ChatGPT?

Plain ChatGPT hallucinates 15-30% of the time on domain-specific questions because it answers from general training data. A well-built RAG system achieves 90-95% factual accuracy because every answer is grounded in your actual documents. The key is proper chunking, embedding selection, and retrieval tuning -- which is what most of my development time goes into.

Can I keep my data on-premise?

Yes. I can deploy the entire RAG pipeline on your infrastructure -- your servers, your cloud account, or even air-gapped environments. The vector database and document pipeline run locally. For the LLM, you can use Azure OpenAI (data stays in your Azure tenant), self-hosted open-source models like Llama, or any GDPR-compliant API provider.

08 / YOUR DEVELOPER

About Kirill Strelnikov

Kirill Strelnikov is a freelance AI engineer based in Barcelona, Spain. He specializes in RAG systems, AI agent development, and AI integration for European businesses. His RAG implementations have automated customer support, powered internal knowledge search, and processed thousands of documents for accurate AI-driven Q&A.

Core stack: Python, LangChain, LlamaIndex, OpenAI, Claude, pgvector, Pinecone, Django, PostgreSQL. Fixed-price contracts with clear deliverables. Communication in English, Spanish, and Russian.

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