DEV Community: Shoaib Iqbal

RAG Systems with Claude: From Documentation to Production

Shoaib Iqbal — Mon, 22 Jun 2026 15:52:33 +0000

Meta: Build production-grade RAG systems using Claude and vector search. Step-by-step guide to document retrieval, embedding, and cost optimization.

The Problem: How Do You Give Claude Your Company's Knowledge?

Claude has a 200K token context window—it can hold an entire book. But what if you need to:

Answer questions about docs that change monthly
Search across thousands of documents efficiently
Stay up-to-date without retraining
Control costs (processing 10MB of text every request is expensive)

A naive approach: throw everything into the prompt. This fails because:

You can't practically include all documents
Costs explode as documents grow
Irrelevant context confuses the model
Updates require new deployments

This is the RAG problem: Retrieval-Augmented Generation.

The Solution: Retrieve Relevant Context, Then Generate

RAG works in two steps:

1. RETRIEVAL: User asks a question
   → Search your documents for relevant context
   → Return top-5 most similar passages

2. GENERATION: Feed Claude the question + retrieved context
   → Claude answers based on your documents
   → Returns answer with citations

This is powerful because:

Only relevant documents are processed (low cost)
Your docs can be updated independently
Answers are grounded in your knowledge
Fully auditable (you see which docs were used)

How Techcologic Builds RAG Systems

We use a three-layer architecture:

Layer 1: Embedding & Vector Search

Step 1: Chunk your documents into passages (500-1000 tokens each)

Document: "Claude API Overview.pdf" (200 pages)
↓
Chunks: [
  "Claude is a large language model trained by Anthropic...",
  "To use Claude, you need an API key from...",
  "Claude 3 family includes Opus, Sonnet, and Haiku...",
  ... (200+ chunks)
]

Step 2: Convert chunks to embeddings

Embedding Service: text-embedding-3-small (or Claude's embedding)
Chunk: "Claude is a large language model..."
↓
Vector: [0.123, -0.456, 0.789, ..., 0.234] (1536 dimensions)

Step 3: Store in vector database

Database: pgvector (PostgreSQL + vector extension)
        OR Pinecone, Weaviate, Milvus (cloud)

Table: documents
├─ id: chunk_id
├─ text: "Claude is a..."
├─ vector: [embeddings]
├─ source: "Claude API Overview.pdf"
└─ updated_at: 2024-06-15

Layer 2: Retrieval on Query

When a user asks a question:

# 1. Embed the user's question
user_question = "How do I use Claude with streaming?"
query_vector = embed_model.embed(user_question)

# 2. Find similar documents in your database
similar = vector_db.search(
    query_vector,
    top_k=5,
    min_similarity=0.7
)

# 3. Result: Top-5 passages from your docs
retrieved = [
    {
        "text": "Claude supports streaming via server-sent events...",
        "source": "API Guide.pdf",
        "similarity": 0.94
    },
    ... (4 more)
]

Layer 3: Generation with Claude

# Construct the augmented prompt
prompt = f"""
Use the following context from Techcologic documentation:

{retrieved_context}

User question: {user_question}

Answer the question using ONLY the context above.
If the answer isn't in the context, say: "I don't have information on this."
Include citations: (Source: document_name)
"""

# Call Claude with your knowledge
response = claude.message(prompt, max_tokens=500)

Real Example: Internal Knowledge Base

Scenario: Techcologic's 50-page engineering handbook, constantly updated.

Without RAG:

Include entire handbook in every prompt (150K tokens)
Cost: $2.25 per query (expensive!)
Fails when handbook exceeds context window

With RAG (Techcologic approach):

Store handbook chunks in vector database
Retrieve only relevant sections per query (2-5K tokens)
Cost: $0.03 per query (75x cheaper!)
Handbook can grow unlimited

Comparison Table:

Approach	Cost per Query	Latency	Scalability	Updates
Naive (full context)	$2-5	5-10s	Limited to token window	Requires redeploy
RAG with pgvector	$0.02-0.05	1-2s	Unlimited docs	Instant
RAG + caching	$0.005-0.01	<500ms	Unlimited docs	Instant

Building RAG Step-by-Step

Step 1: Prepare Documents

1. Collect your documents (PDFs, Markdown, text)
2. Extract text (PyPDF2, pdfplumber for PDFs)
3. Chunk into 500-1000 token pieces
4. Store in database with metadata

Step 2: Set Up Vector Database

Option A: PostgreSQL + pgvector (self-hosted)
Option B: Pinecone (serverless)
Option C: Weaviate (open-source)

We recommend pgvector for most teams—it's cheap, reliable, debuggable.

Step 3: Embed & Index

from anthropic import Anthropic

# Embed each document chunk
embeddings = model.embed(chunks)

# Store in vector DB
vector_db.insert(chunks, embeddings, metadata)

Step 4: Build Retrieval Function

def retrieve_context(question: str, top_k: int = 5):
    query_vector = embed_model.embed(question)
    results = vector_db.search(query_vector, top_k)
    return [r.text for r in results]

Step 5: Create Answer Function

def answer_question(question: str):
    context = retrieve_context(question)
    prompt = f"""Context: {context}

    Question: {question}
    Answer:"""

    response = claude.message(prompt, max_tokens=500)
    return response

Common Pitfalls (and How to Avoid Them)

Problem	Cause	Solution
Low quality answers	Irrelevant documents retrieved	Improve chunking strategy, increase similarity threshold
High costs	Too many tokens sent to Claude	Optimize chunk size, retrieve fewer docs, use caching
Stale answers	Documents never updated	Set up automated sync, monitor freshness
Hallucination	Model invents info not in docs	Use system prompt: "Only answer from provided context"

Techcologic's RAG Stack

For production systems, we use:

Documents → Chunking (LangChain)
         → Embedding (text-embedding-3-small)
         → Storage (pgvector on RDS)
         → Retrieval (vector similarity search)
         → Generation (Claude API)
         → Monitoring (Langsmith, custom logging)

Result: Production RAG systems that handle millions of queries, stay accurate, and cost <$0.02 per question.

Getting Started Today

If you're building with Claude and need to ground answers in your documents:

Start small → Pick 5-10 important docs
Chunk them → 500-token pieces
Embed them → Use OpenAI embeddings or Claude's
Store them → PostgreSQL + pgvector (free tier available)
Test retrieval → Verify top-5 results make sense
Add Claude → Build the augmented prompt
Monitor → Track retrieval quality, token usage

This takes a weekend to prototype, a few days to production.

Ready to ship RAG? Book a Claude architecture call at Techcologic.

Key Takeaways:

RAG lets you augment Claude with your documents
Vector search finds relevant context in milliseconds
Costs drop 10-100x vs. naive approaches
Production RAG systems are reliable and maintainable

Building Production Multi-Agent Systems with Claude

Shoaib Iqbal — Mon, 15 Jun 2026 18:29:09 +0000

Building Production Multi-Agent Systems with Claude

Meta: Learn how to architect production-grade multi-agent systems using Claude API. Covers orchestration, error handling, and real-world deployment patterns.

The Problem: Single-Agent Systems Have Limits

A single Claude call can do amazing things—summarize documents, generate code, answer questions. But many real-world problems require orchestration. You need agents that:

Crawl and validate data from multiple sources
Make decisions based on partial information
Specialize in different tasks (code review, testing, documentation)
Coordinate work across complex workflows

When you try to cram all of this into one prompt, you hit diminishing returns. The model struggles with context, the prompt becomes brittle, and reliability drops.

This is where multi-agent systems shine.

The Solution: Specialized Agents, Orchestrated

A multi-agent system is a collection of focused agents, each optimized for a specific task, coordinated by an orchestrator.

Think of it like a software team:

Product Agent → Understands requirements
Architect Agent → Designs the system
Code Agent → Writes implementation
Test Agent → Validates correctness
Doc Agent → Produces documentation
Orchestrator → Coordinates handoffs, tracks progress

Each agent is small, focused, and excellent at its job. The orchestrator decides who works next, what information to pass, and when the task is complete.

How Techcologic Builds Multi-Agent Systems

We structure Claude multi-agent workflows around three layers:

Layer 1: Specialized Agents

Each agent has:

Clear responsibility (one thing it does well)
Focused prompt (not trying to be everything)
Defined inputs & outputs (structured JSON)
Error handling (knows when to escalate)

Example Agent Prompt:

You are a Code Review Agent.
Input: Pull request code
Task: Review for security, performance, maintainability
Output: JSON with {issues: [], suggestions: []}
Never approve—only assess.

Layer 2: Orchestration Logic

The orchestrator:

Decides agent sequence based on task type
Passes structured data between agents
Retries failed agents with backoffs
Tracks token usage and costs
Escalates when agents can't proceed

Orchestrator Pseudocode:

for agent in workflow_sequence:
    result = call_agent(agent, context)
    if result.error and retries_left:
        result = retry_with_backoff(agent)
    if result.error:
        escalate(agent, result)
    context.add(result.output)

Layer 3: Monitoring & Observability

Production systems need visibility:

Log every agent call
Track latency per agent
Monitor token spend per request
Alert on escalations
Store conversation history for debugging

Real Example: Document Processing Pipeline

Task: Ingest a 100-page PDF, extract requirements, generate implementation plan.

Old way (single agent):

Prompt: 50KB of instructions
Success rate: 60%
Cost: $2-5 per document
Debugging: nightmare

Multi-agent way (Techcologic approach):

Extraction Agent → Pull raw text, tables, figures
Classification Agent → Identify section types (requirements, design, appendix)
Synthesis Agent → Combine related sections, resolve contradictions
Planning Agent → Generate implementation roadmap
QA Agent → Verify completeness, flag gaps

Results:

Success rate: 95%+
Cost: $0.40 per document
Debugging: clear where failures happen
Latency: 45 seconds (parallelizable)

Why This Matters for SaaS

Multi-agent systems are how you:

Scale AI features without hitting prompt-engineering limits
Build reliability (each agent can be tested independently)
Control costs (focused models work faster, cheaper)
Debug failures (know which agent failed and why)
Adapt quickly (swap agents, change workflows, not rewrite prompts)

Getting Started

If you're building with Claude and hitting walls:

Map your workflow → What sequential steps does a human need?
Identify agents → One agent per step
Test each agent → Individually, with diverse inputs
Build orchestrator → Call agents in sequence, handle errors
Add observability → Log everything, measure success rate

The investment in architecture pays back in reliability and cost.

Ready to Build?

At Techcologic, we've shipped multi-agent systems for event intelligence platforms, mentoring systems, and B2B marketplaces. If you're building something that needs coordinated AI reasoning, book a 30-minute Claude architecture call.

We design the system, you launch in weeks—not quarters.

Key Takeaways:

Single agents have limits; multi-agent systems scale
Specialization + orchestration = reliability
Production systems need observability
Costs drop when agents stay focused

Share this with your team if you're building with Claude.