Retrieval-Augmented Generation (RAG) represents a paradigm shift in how artificial intelligence systems access and utilize information. By combining the generative capabilities of large language models with dynamic information retrieval from external knowledge bases, RAG systems overcome the fundamental limitations of standalone language models—namely, their reliance on static training data and tendency toward hallucination.

This document provides a comprehensive technical reference covering the essential concepts, components, and implementation patterns that form the foundation of modern RAG architectures. Each concept is presented with clear explanations, practical code examples in Go, and real-world considerations for building production-grade systems.

Whether you are architecting a new RAG system, optimizing an existing implementation, or seeking to understand the theoretical underpinnings of retrieval-augmented approaches, this reference provides the knowledge necessary to build accurate, efficient, and trustworthy AI applications. The concepts range from fundamental building blocks like embeddings and vector databases to advanced techniques such as hybrid search, re-ranking, and agentic RAG architectures.

As the field of artificial intelligence continues to evolve, RAG remains at the forefront of practical AI deployment, enabling systems that are both powerful and grounded in verifiable information. This document serves as your guide to mastering these critical technologies.

Core Concepts and Implementation Patterns

Generator (Language Model)

The component that generates the final answer using the retrieved context.

Retrieval

Retrieval is the process of identifying and extracting relevant information from a knowledge base before generating a response. It acts as the AI’s research phase, gathering necessary context from available documents before answering.

Rather than relying solely on pre-trained knowledge, retrieval enables the AI to access up-to-date, domain-specific information from documents, databases, or other knowledge sources.

In the example below, the retriever selects the top five most relevant documents and provides them to the LLM to generate the final answer.

relevantDocs := vectorDB.Search(query, 5) // top_k=5
answer := llm.Generate(query, relevantDocs)

Embeddings

Embeddings are numerical representations of text that capture semantic meaning. They convert words, sentences, or documents into dense vectors that preserve context and relationships.

The example below demonstrates how to generate embeddings using the OpenAI API.

import (
    "context"
    "github.com/sashabaranov/go-openai"
)

client := openai.NewClient("your-token")
resp, err := client.CreateEmbeddings(
    context.Background(),
    openai.EmbeddingRequest{
        Input: []string{"Retrieval-Augmented Generation"},
        Model: openai.SmallEmbedding3,
    },
)
if err != nil {
    log.Fatal(err)
}
vector := resp.Data[0].Embedding

Vector Databases

Vector databases are specialized systems designed to store and query high-dimensional embeddings. Unlike traditional databases that rely on exact matches, they use distance metrics to identify semantically similar content.

They support fast similarity searches across millions of documents in milliseconds, making them essential for scalable RAG systems.

The example below shows how to create a collection and add documents with embeddings using the Chroma client.

import "github.com/chroma-core/chroma-go"

client := chroma.NewClient()
collection, _ := client.CreateCollection("docs")

// Generate embeddings for documents
docs := []string{"RAG improves accuracy", "LLMs can hallucinate"}
emb1 := embedder.Embed(docs[0])
emb2 := embedder.Embed(docs[1])

// Add documents with their embeddings
collection.Add(
    context.Background(),
    chroma.WithIDs([]string{"doc1", "doc2"}),
    chroma.WithEmbeddings([][]float32{emb1, emb2}),
    chroma.WithDocuments(docs),
)

Retriever

A retriever is a component that manages the retrieval process. It converts a user query into an embedding, searches the vector database, and returns the most relevant document chunks.

It functions like a smart librarian, understanding the query and locating the most relevant information within a large collection.

The example below demonstrates a basic retriever implementation.

type Retriever struct {
    VectorDB VectorDB
}

func (r *Retriever) Retrieve(query string, topK int) []Result {
    queryVector := Embed(query)
    return r.VectorDB.Search(queryVector, topK)
}

Chunking

Chunking is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.

The example below demonstrates a character-based chunking function with overlap support.

func ChunkText(text string, chunkSize, overlap int) []string {
    var chunks []string
    runes := []rune(text)
    for start := 0; start < len(runes); start += (chunkSize - overlap) {
        end := start + chunkSize
        if end > len(runes) {
            end = len(runes)
        }
        chunks = append(chunks, string(runes[start:end]))

        if end >= len(runes) {
            break
        }
    }
    return chunks
}

chunks := ChunkText(document, 500, 50)

Context Window

The context window is the maximum number of tokens (words or subwords) an LLM can process in a single request. It defines the model’s working memory and the amount of context that can be included.

Context windows range from 4K tokens in older models to over 200K in modern ones. Retrieved chunks must fit within this limit, making chunk size and selection critical.

The example below demonstrates how to fit chunks within a token limit.

func FitContext(chunks []string, maxTokens int) []string {
    var context []string
    tokenCount := 0

    for _, chunk := range chunks {
        chunkTokens := CountTokens(chunk)
        if tokenCount + chunkTokens > maxTokens {
            break
        }
        context = append(context, chunk)
        tokenCount += chunkTokens
    }

    return context
}

Grounding

Grounding ensures AI responses are based on retrieved, verifiable sources rather than hallucinated information. It keeps the model anchored to real data.

Effective grounding requires citing specific sources and relying only on the provided context to support claims. This reduces hallucinations and improves trustworthiness.

The example below demonstrates a grounding prompt template.

prompt := fmt.Sprintf(`
Answer the question using ONLY the provided context.
Cite the source for each claim.
Context: %s

Question: %s

Answer with citations:
`, retrievedDocs, userQuestion)

response := llm.Generate(prompt)

Re-Ranking

Two-stage retrieval enhances result quality by combining speed and precision. First, a fast initial search retrieves many candidates (e.g., top 100). Then, a more accurate cross-encoder model re-ranks them to identify the best matches.

This approach pairs broad retrieval with fine-grained scoring for optimal results.

The example below demonstrates a basic re-ranking workflow.

// Initial fast retrieval
candidates := retriever.Search(query, 100)

// Re-rank using a CrossEncoder
scores := reranker.Predict(query, candidates)

// Sort candidates by score and take top 5
topDocs := SortByScore(candidates, scores)[:5]

Hybrid Search

Hybrid search combines keyword-based search (BM25) with semantic vector search. It leverages both exact term matching and meaning-based similarity to improve retrieval accuracy.

By blending keyword and semantic scores, it provides the precision of exact matches along with the flexibility of understanding conceptual queries.

The example below demonstrates a hybrid search implementation.

func HybridSearch(query string, alpha float64) []Result {
    keywordResults := BM25Search(query)
    semanticResults := VectorSearch(query)

    // Combine scores:
    // finalScore = alpha * keywordScore + (1-alpha) * semanticScore
    finalResults := CombineAndRank(keywordResults, semanticResults, alpha)

    return finalResults[:5]
}

Metadata Filtering

Metadata filtering narrows search results by using document attributes such as dates, authors, types, or departments before performing a semantic search. This reduces noise and improves precision.

Applying filters like author: John Doe or document_type: report focuses the search on the most relevant documents.

The example below demonstrates metadata filtering in a vector database query.

results := collection.Query(
    Query{
        Texts: []string{"quarterly revenue"},
        TopK: 10,
        Where: map[string]interface{}{
            "year":       2024,
            "department": "sales",
            "type": map[string]interface{}{
                "$in": []string{"report", "presentation"},
            },
        },
    },
)

Similarity Search

The retriever is the core search mechanism in RAG, identifying documents whose embeddings are most similar to a query’s embedding. It evaluates semantic closeness rather than just keyword matches.

Similarity is typically measured using cosine similarity (angle between vectors) or dot product, with higher scores indicating more relevant content.

The example below demonstrates cosine similarity using the Gonum library.

import (
    "gonum.org/v1/gonum/mat"
)

func CosineSimilarity(vec1, vec2 []float64) float64 {
    v1 := mat.NewVecDense(len(vec1), vec1)
    v2 := mat.NewVecDense(len(vec2), vec2)

    dotProduct := mat.Dot(v1, v2)
    norm1 := mat.Norm(v1, 2)
    norm2 := mat.Norm(v2, 2)

    return dotProduct / (norm1 * norm2)
}

// Usage example
queryVec := Embed(query)
for _, docVec := range documentVectors {
    score := CosineSimilarity(queryVec, docVec)
    // Store score for ranking
}

Prompt Injection

Prompt injection is a security vulnerability where malicious users embed instructions in queries to manipulate AI behavior. Attackers may attempt to override system prompts or extract sensitive information.

Common examples include phrases like “ignore previous instructions” or “reveal your system prompt.” RAG systems must sanitize inputs to prevent such attacks.

The example below demonstrates a basic input sanitization function. In production, multiple defenses—such as regex patterns, semantic similarity checks, and output validation—are required.

func SanitizeInput(userInput string) (string, error) {
    // Basic pattern matching - extend with regex for production use
    dangerousPatterns := []string{
        "ignore previous instructions",
        "disregard system prompt",
        "reveal your instructions",
        "ignore all prior",
        "bypass security",
    }

    lowerInput := strings.ToLower(userInput)
    for _, pattern := range dangerousPatterns {
        if strings.Contains(lowerInput, pattern) {
            return "", errors.New("invalid input detected")
        }
    }

    // Additional checks for production:
    // - Regex for obfuscated patterns (e.g., "ign0re")
    // - Semantic similarity to known attack phrases
    // - Length and character validation

    return userInput, nil
}

Hallucination

Generative AI can produce convincing but incorrect information, including false facts, fake citations, or invented details.

RAG helps reduce hallucinations by grounding responses in retrieved documents, though proper grounding and citation are essential to minimize risk.

The example below demonstrates a verification function that checks whether a response is supported by source documents. For higher reliability, consider using Natural Language Inference models or extractive fact-checking, as relying on one LLM to verify another has limitations.

func IsSupported(response, sourceDocs string) bool {
    verificationPrompt := fmt.Sprintf(`
    Response: %s
    Source: %s

    Is this response fully supported by the source documents?
    Answer yes or no.
    `, response, sourceDocs)

    result := llm.Generate(verificationPrompt)
    return strings.ToLower(strings.TrimSpace(result)) == "yes"
}

// Alternative: Use NLI model for more reliable verification
func IsSupportedNLI(response, sourceDocs string) bool {
    // NLI models classify as: entailment, contradiction, or neutral
    result := nliModel.Predict(sourceDocs, response)
    return result.Label == "entailment" && result.Score > 0.8
}

Agentic RAG

Agentic RAG is an advanced architecture where the AI actively plans, reasons, and controls its own retrieval strategy. Rather than performing a single search, the agent can conduct multiple searches, analyze results, and iterate.

It autonomously decides what information to retrieve, when to search again, which tools to use, and how to synthesize multiple sources—enabling complex, multi-step reasoning.

The example below demonstrates an agentic RAG implementation.

func (a *AgenticRAG) Answer(query string) string {
    plan := a.llm.CreatePlan(query)

    for _, step := range plan.Steps {
        switch step.Action {
        case "search":
            results := a.retriever.Search(step.Query)
            a.context.Add(results)
        case "reason":
            analysis := a.llm.Analyze(a.context)
            a.context.Add(analysis)
        }
    }

    return a.llm.Synthesize(a.context)
}

Latency

RAG latency is the total time from a user query to the final response, including embedding generation, vector search, re-ranking (if used), and LLM generation. Each step contributes to the delay.

Latency directly impacts user experience and can be optimized by caching embeddings, using faster models, narrowing search scope, and parallelizing operations. Typical RAG systems aim for sub-second to a few seconds of latency.

The example below measures latency for each stage of the RAG pipeline.

import "time"

func MeasureLatency(query string) {
    start := time.Now()

    // Step 1: Embed query
    embedding := Embed(query)
    t1 := time.Now()

    // Step 2: Search
    results := vectorDB.Search(embedding)
    t2 := time.Now()

    // Step 3: Generate
    response := llm.Generate(query, results)
    t3 := time.Now()

    fmt.Printf("Embed: %v | Search: %v | Generate: %v\n",
        t1.Sub(start), t2.Sub(t1), t3.Sub(t2))
}

What’s Next?

Our open source (under the PostgreSQL license) RAG server for PostgreSQL is hosted on GitHub, free to use. Stop by and star the repository if you want to watch for future releases and features: https://github.com/pgEdge/pgedge-rag-server

Many more open source tools are available in our GitHub that help you build AI applications that you can ship to production with confidence. Check them out: https://github.com/pgEdge/

Essential Concepts for Retrieval-Augmented Generation (RAG)

Core Concepts and Implementation Patterns

Generator (Language Model)

Retrieval

Embeddings

Vector Databases

Retriever

Chunking

Context Window

Grounding

Re-Ranking

Hybrid Search

Metadata Filtering

Similarity Search

Prompt Injection

Hallucination

Agentic RAG

Latency

What’s Next?

Get started today.

Essential Concepts for Retrieval-Augmented Generation (RAG)

Core Concepts and Implementation Patterns

Generator (Language Model)

Retrieval

Embeddings

Vector Databases

Retriever

Chunking

Context Window

Grounding

Re-Ranking

Hybrid Search

Metadata Filtering

Similarity Search

Prompt Injection

Hallucination

Agentic RAG

Latency

What’s Next?

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