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The architecture behind grounded AI

RAG development services: what retrieval-augmented generation is, and how I build it

Retrieval-augmented generation is the architecture behind every AI system that needs to answer accurately from your own data instead of guessing from general training knowledge. I design and build production RAG systems: document ingestion, retrieval engineering, generation, guardrails, and deployment.

Experience5+ yrs production RAG
Outcome60–80% less hallucination
Turnaround48h written spec
Talk to a RAG developer View case studies

Every answer traces to a source, guardrails included from day one

grounding preview, illustrative live
LowHallucination risk
4Chunks retrieved
Live docsAnswer basis
Generated answer

    Grounded, not guessedEvery answer is retrieved from your own documents at query time, not the model's fixed training memory.
    Source-traceableEach response can cite the exact document it came from, for review and for trust.
    Guardrails includedConfidence thresholds and a defensive "I don't know" fallback ship with every build, not bolted on later.
    No retraining requiredUpdate the knowledge base, not the model. Documents change daily, RAG keeps up without a training run.

    Plain answer

    What is RAG (retrieval-augmented generation)?

    Definition

    Retrieval-augmented generation (RAG) is an AI architecture that improves large language model responses by retrieving relevant information from an external knowledge base at query time and inserting it into the model's context before it generates an answer. Instead of relying only on what the model learned during training, a fixed and eventually outdated snapshot, RAG grounds each response in specific, current, and verifiable source content, making answers more accurate, current, and traceable back to a source document.

    Step by step

    How RAG works

    The same five-step pipeline animating in the signature panel above, applied to your own documents.

    STEP 1

    Query submitted

    A user asks a question in natural language.

    STEP 2

    Query embedded

    Converted into a numerical embedding capturing its meaning.

    STEP 3

    Vector search

    The system finds the most relevant chunks in a vector database.

    STEP 4

    Context inserted

    Retrieved content joins the query in the LLM's context window.

    STEP 5

    Grounded answer

    The LLM generates a response with sources available to cite.

    The decision

    RAG vs plain LLM vs fine-tuning

    These are frequently combined: a fine-tuned model for task-specific behavior, grounded by RAG for current or proprietary data access.

    Factor Plain LLM RAG Fine-tuning
    Knowledge source Fixed training data Training data plus live retrievable content Training data plus new task-specific behavior
    Update process Requires retraining Add or update documents, no retraining Requires retraining on new examples
    Best for General knowledge tasks Domain-specific, current, or private data access Changing model style, tone, or task behavior
    Traceability No source citation Can cite exact source document No source citation

    What I build

    RAG development services

    Each layer of a production RAG pipeline, built and tested as its own verifiable stage.

    BUILD

    Document ingestion & chunking

    Building the pipeline that prepares your source content, PDFs, wikis, databases, support tickets, into properly chunked, retrievable pieces. This step determines the ceiling on everything downstream.

    BUILD

    Vector database architecture

    Selecting and configuring the right vector store (Pinecone, Weaviate, Qdrant, FAISS, pgvector) for your scale, latency, and hosting requirements.

    BUILD

    Retrieval engineering

    Building the retrieval layer: hybrid search (semantic plus keyword), metadata filtering, and reranking to maximize the relevance of what gets passed to the LLM.

    BUILD

    Generation & guardrails

    Configuring the generation layer with grounding constraints, defensive fallback behavior, and confidence thresholds so the system says "I do not know" instead of guessing.

    EVALUATE

    RAG evaluation

    Testing retrieval and generation quality against your real queries using RAGAS, DeepEval, or a custom evaluation set, with results shared transparently before launch.

    BUILD

    Production deployment

    Deploying the full RAG pipeline as a versioned, monitored API integrated with your product or internal tools.

    Why it matters

    Why businesses need RAG

    A plain LLM has no access to your private, proprietary, or real-time information, and will either say it does not know or generate a plausible-sounding but incorrect answer. RAG solves this directly by giving the model exactly the information it needs, retrieved fresh at query time, without the cost and delay of retraining the model every time your underlying information changes.

    COMMON APPLICATIONS

    Where I've built RAG systems

    • Customer support chatbots grounded in product documentation and policies
    • Internal knowledge assistants for employee Q&A over wikis and HR policies
    • Healthcare AI assistants grounded in clinical guidelines — see the healthcare page
    • Legal and compliance research tools citing specific clauses or precedents
    • Ecommerce product Q&A answering from a live product catalog
    • Enterprise search surfacing answers, not just links, across large repositories

    How it gets built

    Process

    PHASE 01days 1 to 2

    Discovery

    +
    Understanding your data sources, use case, and what "accurate" needs to mean for your application.
    PHASE 02within 48h

    Architecture spec

    +
    A written technical spec covering retrieval strategy, vector database choice, and guardrail design, delivered within 48 hours.
    PHASE 03the decisive one

    Build in milestones

    +
    Ingestion, retrieval, generation, and guardrails built and tested as separate, verifiable stages.
    PHASE 04before launch

    Evaluation

    +
    Testing against real queries with transparent results before launch.
    PHASE 0530 days plus

    Deployment & support

    +
    Production deployment with monitoring and a post-launch support window.

    Investment

    Pricing

    Engagement type What's included Typical investment
    RAG proof-of-concept Baseline retrieval pipeline on a data sample, evaluation report $1,500 to $4,000
    Production RAG system Full pipeline, guardrails, deployment, documentation $8,000 to $30,000
    RAG system audit Diagnose and improve an underperforming existing system $1,000 to $3,000
    Hourly consulting Architecture review, retrieval strategy design $75 to $150/hr

    Retrieval-grounded systems delivered include the AI Customer Feedback Classification pipeline and AI Video Summarizer. Full case studies at shreyans.tech/ai-case-studies.

    FAQ

    Frequently asked questions

    What does RAG stand for?
    RAG stands for retrieval-augmented generation, an AI architecture that retrieves relevant information before an LLM generates its response, rather than relying only on the model's training data.
    Is RAG the same as fine-tuning?
    No. Fine-tuning changes a model's weights through additional training. RAG retrieves external information and adds it to the model's context at query time without changing the model. They solve different problems and are often used together.
    Does RAG eliminate AI hallucination?
    RAG significantly reduces hallucination, typically 60 to 80% versus a bare LLM, by grounding responses in retrieved content, but does not eliminate it entirely. Guardrails and confidence thresholds are layered on top in every production system I build.
    How much does RAG development cost?
    A proof-of-concept starts at $1,500 to $4,000. A full production RAG system ranges from $8,000 to $30,000 depending on complexity and data volume. Contact for a fixed-price estimate specific to your use case.
    How long does it take to build a RAG system?
    A proof-of-concept typically takes 2 to 4 weeks. A full production system with guardrails, evaluation, and deployment typically takes 4 to 10 weeks depending on data complexity and integration scope.
    AI Summarizer