Retrieval augmented generation (RAG) is a technique that helps improve the accuracy and reliability of large language models (LLMs) by incorporating information from external sources.
Retrieval
When a user provides a prompt to an LLM with RAG capabilities, the system searches for relevant information in an external knowledge base.
Augmentation
This retrieved information is used to supplement the LLM’s internal knowledge. Basically, it’s giving the LLM additional context to work with.
Generation
Finally, the LLM uses its understanding of language and the augmented information to generate a response to the user query.
Flexibility
RAG systems can be easily adapted to different domains by simply adjusting the external data sources. This allows for the rapid deployment of generative AI solutions in new areas without extensive LLM retraining.
Simpler system maintenance
Updating the knowledge base in a RAG system is typically easier than retraining an LLM. This simplifies maintenance and ensures the system remains current with the latest information.
Control over knowledge sources
Unlike LLMs trained on massive datasets of unknown origin, RAG implementation allows you to choose the data sources the LLM uses.
1 step – Assessment
We’ll start by discussing your specific goals and desired outcomes for the LLM application.
2 step – Data gathering and prompt engineering
Our data engineering team will clean, preprocess, and organize your new data sources.
3 step – Retrieval system setup
Then, we’ll set up a retrieval system that can efficiently search and deliver relevant information to the LLM based on its prompts and queries.
4 step – LLM integration
After that, we’ll integrate your existing LLM with the RAG system.
5 step – Prompt design
Our NLP experts will collaborate with you to design effective prompts and instructions for the LLM.
6 step – Training
We’ll train and fine-tune the RAG system to improve the quality and accuracy of its generated text.
7 step – Evaluation
Our team will continually evaluate the system’s outputs, ensuring they meet your requirements.
8 step – Refinement
Based on this evaluation, we might refine the data sources, retrieval methods, or prompts to optimize the overall effectiveness of the RAG system.
9 step – Ongoing support
We’ll monitor system health, addressing any technical issues, and staying updated on the latest advancements in RAG technology.
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Our client is a technology company specializing in providing services for industries with highly regulated communications. Their AI-based expertise allows to make those processes more simple. Geniusee was entrusted with migrating client’s infrastructure from Azure to Amazon, aiming to consolidate their crucial infrastructure in one place. The project involved migrating their database and leveraging Amazon services to optimize the system's scalability, security, and performance.
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What is the difference between RAG and LLM?
Retrieval augmented generation (RAG) combines retrieval-based and generative models, using external knowledge sources to generate contextually relevant responses. Large Language Models (LLMs), on the other hand, rely solely on internal training data to generate LLM responses.
Retrieval augmented generation excels in handling complex queries by incorporating external context, while LLMs generate responses based solely on their learned patterns.
What is the RAG method for LLM?
The RAG method for LLMs involves improving the generative capabilities of LLMs by extending the RAG mechanism.
What is the example of RAG?
Example: retrieval augmented generation in customer service. RAG can allow chatbots to find relevant information (like return policies) from a company’s database and combine it with their knowledge to answer questions accurately.
What are the advantages of RAG LLM?
More accurate answers: RAG verifies information with real-world sources, reducing factual errors and hallucinations (made-up info) from LLMs.
Up-to-date knowledge: RAG can access constantly updated information, unlike static LLM training data.
Increased trust: users can see the sources used to generate responses, making rag outputs more believable.
