RAG Systems for Web Projects: Complete Implementation Guide

Retrieval-Augmented Generation (RAG) fundamentally improves how AI applications handle knowledge. Rather than relying solely on an LLM’s training data (which becomes outdated), RAG retrieves relevant information at runtime through semantic search, then uses that context to generate accurate responses.

This guide covers RAG implementation from architecture to production deployment, with practical examples for Laravel, Shopify, and WordPress applications.

Understanding RAG Architecture

The Core Problem RAG Solves

Large language models have inherent limitations:

Knowledge Cutoff: Models trained on data up to a specific date lack current information. A model trained in 2023 won’t know about events in 2024.

Hallucination Risk: When LLMs don’t know something, they often generate plausible-sounding but incorrect information rather than admitting uncertainty.

Private Data Gap: Your proprietary documentation, code, or business knowledge isn’t in the model’s training data.

Computational Cost: Fine-tuning models on your specific data is expensive, time-consuming, and quickly becomes outdated.

RAG solves these by retrieving relevant information at query time, giving the model fresh, accurate context.

How RAG Works: The Complete Pipeline

1. Knowledge Ingestion

Transform your content into searchable embeddings:

# Example: Processing documentation
documents = load_documents('./docs')

for doc in documents:
    # Chunk into semantic units
    chunks = chunk_document(doc, size=512, overlap=50)

    for chunk in chunks:
        # Generate embedding
        embedding = openai.embeddings.create(
            model="text-embedding-3-small",
            input=chunk.content
        )

        # Store in vector database
        pinecone.upsert([{
            'id': chunk.id,
            'values': embedding.data[0].embedding,
            'metadata': {
                'content': chunk.content,
                'source': doc.path,
                'section': chunk.heading
            }
        }])

2. Query Embedding

Convert user questions into the same vector space:

const queryEmbedding = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: userQuery
});

3. Semantic Retrieval

Find the most relevant chunks using cosine similarity:

const searchResults = await pinecone.query({
  vector: queryEmbedding.data[0].embedding,
  topK: 5,
  includeMetadata: true
});

4. Context-Augmented Generation

Inject retrieved content into the LLM prompt:

const context = searchResults.matches
  .map(match => match.metadata.content)
  .join('\n\n---\n\n');

const completion = await openai.chat.completions.create({
  model: "gpt-4-turbo",
  messages: [{
    role: "system",
    content: `Answer using only the provided context. If the context doesn't contain the answer, say so.`
  }, {
    role: "user",
    content: `Context:\n${context}\n\nQuestion: ${userQuery}`
  }]
});

This complete pipeline ensures responses are grounded in your actual data, not model hallucinations.

RAG vs Fine-Tuning vs Prompt Engineering

RAG Advantages:

When to Use Fine-Tuning Instead:

• Changing model behaviour or writing style
• Teaching domain-specific patterns
• Improving performance on specific tasks

When Prompt Engineering Suffices:

• Simple context fits in prompt window
• No frequent knowledge updates needed
• Cost-sensitive applications

For prompt best practices, see our prompt engineering guide.

Example Stack

• Embedding model: OpenAI text-embedding-3-small, Cohere, or InstructorXL
• Vector DB: Pinecone, Weaviate, Qdrant, or pgvector (Postgres extension)
• LLM: GPT-4, Claude, Mistral, or local models
• Frameworks: LangChain, LlamaIndex, or custom TypeScript / Laravel code

Data Ingestion Tips

• Chunk by semantic structure (headings, paragraphs)
• Add metadata (source, page, section)
• Use aggressive deduplication
• Use batch upserts for performance

Prompt Strategy

Wrap retrieved content in clear markers:

### Context
[CHUNK 1]
[CHUNK 2]

### Question
[USER INPUT]

Instruct the LLM to answer only using provided context.

Real-World Application

In our Shopify RAG assistant:

• Theme code, metafields, and docs embedded
• Users asked questions like “How does this snippet impact CLS?”
• Context + code inserted into prompt
• Model responded using relevant data only

Accuracy improved >60% over default prompts.

Monitoring & Optimisation

• Use cosine similarity thresholds
• Track embedding drift if sources change
• Compress old chunks to reduce cost

Related Reading

• Prompt Engineering Best Practices
• Setting up MCP