RAG by code: how to reduce hallucinations and reuse snippets

Introduction

One of the most discussed issues in the use of AI in software development concerns hallucinations: the model generates syntactically correct code but logically incorrect or out-of-context. This happens because AI does not have direct access to the repository and bases its responses solely on training data. The solution is RAG for code (Retrieval Augmented Generation), an approach that integrates a semantic search engine with the language model to retrieve real snippets and functions from the project.

The RAG for code represents one of the most interesting applications of sviluppo-AI, as it combines the power of generative models with the practicality of existing code.

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What is RAG for code

The Retrieval Augmented Generation (RAG) is a technique that combines two phases:

1. Retrieval → the system searches the archives (repository, database, documentation) for the most relevant code snippets.
2. Generation → the AI model uses these snippets as context to produce a coherent and accurate response.

In the context of software development, the RAG for code allows for:

• Reduce logical and syntactical errors.
• Reuse already tested snippets.
• Obtain responses that are more aligned with the style and libraries used by the team.

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How it works in practice

Let’s imagine a team using Cursor or GitHub Copilot. Without RAG, the AI suggests “generic” solutions based on training. With RAG for code instead:

• The developer asks a question → e.g. “How do I implement a connection to PostgreSQL with pooling?”
• The RAG engine searches the snippets in the repository and finds a similar function already used.
• The AI combines the found context with generation and proposes a solution that adheres to the actual project.

In this way, the result is not only syntactically correct but also aligned with the business stack.

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Implementation Example

Here is a basic template in Python for building a RAG prototype for code:

from openai import OpenAI
from my_vector_db import search_snippets  # funzione che interroga un DB vettoriale

client = OpenAI(api_key="API_KEY")

query = "Funzione Python per leggere da un file CSV e inserire in PostgreSQL"

# Recupero snippet rilevanti
docs = search_snippets(query, top_k=3)

context = "\n".join([d["content"] for d in docs])

prompt = f"Usa i seguenti snippet come contesto e genera una funzione ottimizzata:\n{context}\n\nDomanda: {query}"

response = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": prompt}]
)

print(response.choices[0].message.content)

In this example, the RAG for code retrieves snippets from a vector database (e.g., based on Pinecone, Weaviate, or PostgreSQL with the pgvector extension) and provides them to the OpenAI model as context.

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Benefits of RAG for Code

The main advantages are:

• Reduction of hallucinations: AI is based on real code.
• Reuse: maximizes the value of already validated snippets and functions.
• Consistency: maintains the style and conventions of the team.
• Efficiency: accelerates writing, debugging, and maintenance.

These benefits strengthen the role of the RAG as an essential component in AI-development pipelines.

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Limits and Considerations

Despite the advantages, RAG for code presents some challenges:

• Knowledge Base Management → duplicate or poorly documented snippets reduce the quality of responses.
• Performance → semantic search on large repositories requires efficient indexing.
• Privacy and Security → be careful not to send sensitive code to non-compliant cloud services.

It is therefore essential to have solid governance, which is an integral part of sviluppo-AI practices.

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Integration into the DevOps Flow

To make the RAG part of the software lifecycle:

• Automatically index the repository on every commit.
• Integrate the RAG engine with tools like Cursor or Copilot.
• Use CI/CD to validate that the generated responses are compliant.
• Monitor quality metrics (precision, recall, developer feedback).

Thus, the RAG for code becomes a living practice, not an isolated experiment.

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Consulting and Services

Implementing a RAG system for code requires expertise in AI, vector databases, and DevOps. As an IT consultant, I offer:

• Audit of repositories and creation of the snippet base.
• Setup of vector databases (pgvector, Pinecone, Weaviate).
• Integration of RAG with AI such as OpenAI, Claude, or LLaMA.
• Training of teams on governance and security in sviluppo-AI.

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Conclusion

The RAG per codice is the concrete answer to the problem of hallucinations and a powerful tool for reusing existing assets. Integrating it into DevOps workflows means increasing productivity, consistency, and software quality.

👉 Want to understand how to apply RAG to your repository? Learn more in our complete guide to AI development or contact me for a personalized consultation.

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FAQ

What is RAG for code?
It is a technique that combines semantic search and AI generation to reduce hallucinations and reuse snippets.

How to implement a RAG for code?
By indexing the repository in a vector database and providing snippets to the AI model as context.

Is the RAG for code secure?
Yes, if managed in compliant environments (e.g., on-premise or certified cloud) and with governance of sviluppo-AI.