RAG Explained: Give an LLM Your Own Knowledge
📰 Dev.to · Devanshu Biswas
Learn how to use RAG to give an LLM your own knowledge, enabling it to answer questions based on your private data or company docs
Action Steps
- Embed a question into a vector using techniques from previous posts on embeddings and vector databases
- Retrieve the top-k most similar chunks from your knowledge base
- Augment the prompt with system instructions and the retrieved chunks as context
- Generate an answer based on the augmented prompt
- Test and refine the RAG pipeline to ensure accurate results
Who Needs to Know This
Developers and data scientists working with LLMs can benefit from understanding RAG to improve the accuracy of their models, especially when dealing with private or company-specific data
Key Insight
💡 RAG allows LLMs to access and utilize private or company-specific data, reducing the likelihood of made-up answers
Share This
🤖 Improve LLM accuracy with RAG! Retrieve, augment, and generate answers based on your own knowledge 📚
Key Takeaways
Learn how to use RAG to give an LLM your own knowledge, enabling it to answer questions based on your private data or company docs
Full Article
Title: RAG Explained: Give an LLM Your Own Knowledge
URL Source: https://dev.to/dev48v/rag-explained-give-an-llm-your-own-knowledge-388h
Published Time: 2026-06-24T14:53:55Z
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[Devanshu Biswas](https://dev.to/dev48v)
Posted on Jun 24
# RAG Explained: Give an LLM Your Own Knowledge
[#ai](https://dev.to/t/ai)[#rag](https://dev.to/t/rag)[#llm](https://dev.to/t/llm)[#beginners](https://dev.to/t/beginners)
An LLM doesn't know your company's docs, last week's news, or your private data — and it'll confidently make things up. RAG fixes that: retrieve the relevant facts, put them in the prompt, then generate. It's the backbone of almost every real LLM app. Here's the full pipeline, live.
📚 **See it answer from a private knowledge base:**[https://dev48v.infy.uk/ai/days/day15-rag.html](https://dev48v.infy.uk/ai/days/day15-rag.html)
## [](https://dev.to/dev48v/rag-explained-give-an-llm-your-own-knowledge-388h#retrieve-%E2%86%92-augment-%E2%86%92-generate) Retrieve → Augment → Generate
1. **Embed the question** into a vector (last two posts: embeddings + vector DBs).
2. **Retrieve** the top-k most similar chunks from your knowledge base.
3. **Augment** the prompt: system instructions + the retrieved chunks as contex
URL Source: https://dev.to/dev48v/rag-explained-give-an-llm-your-own-knowledge-388h
Published Time: 2026-06-24T14:53:55Z
Markdown Content:
[Skip to content](https://dev.to/dev48v/rag-explained-give-an-llm-your-own-knowledge-388h#main-content)
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[Devanshu Biswas](https://dev.to/dev48v)
Posted on Jun 24
# RAG Explained: Give an LLM Your Own Knowledge
[#ai](https://dev.to/t/ai)[#rag](https://dev.to/t/rag)[#llm](https://dev.to/t/llm)[#beginners](https://dev.to/t/beginners)
An LLM doesn't know your company's docs, last week's news, or your private data — and it'll confidently make things up. RAG fixes that: retrieve the relevant facts, put them in the prompt, then generate. It's the backbone of almost every real LLM app. Here's the full pipeline, live.
📚 **See it answer from a private knowledge base:**[https://dev48v.infy.uk/ai/days/day15-rag.html](https://dev48v.infy.uk/ai/days/day15-rag.html)
## [](https://dev.to/dev48v/rag-explained-give-an-llm-your-own-knowledge-388h#retrieve-%E2%86%92-augment-%E2%86%92-generate) Retrieve → Augment → Generate
1. **Embed the question** into a vector (last two posts: embeddings + vector DBs).
2. **Retrieve** the top-k most similar chunks from your knowledge base.
3. **Augment** the prompt: system instructions + the retrieved chunks as contex
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