Fresh Data And Context For Your LLM: Retrieval Augmented Generation
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Fresh Data And Context For Your LLM: Retrieval Augmented Generation
Generate More Tailored Responses And Fewer Hallucinations
On October 24, Harpreet Sahota (Developer Relations Expert) joined me on “What’s the BUZZ?” and shared how you can augment your Generative AI model with new data. Retrieval Augmented Generation (RAG) and fine-tuning are two key concepts for providing Large Language Models (LLM) access to new data, for example your company’s travel and procurement policies, or other business data. RAG offers cost efficiency and reduced technical complexity compared to fine-tuning. But every choice comes with trade-offs. What are they and what do AI leaders need to know about RAG? Here is what we’ve talked about…
Expanding Knowledge Horizons With Retrieval Augmented Generation
RAG is a concept which enables an LLM to query external databases for relevant information, enhancing response generation. It is similar to consulting books for quick knowledge refreshers.
RAG addresses limitations inherent in pre-trained models, such as fixed knowledge cutoff dates and the generation of overly generic content. It ensures timeliness, relevance, and factual responses, augmenting the capabilities of large language models with context-aware answers and source attribution.
The five key components of the RAG pipeline include:
indexing,
query transformation,
retrieval,
filtering, and
improved context.
Fine-tuning involves refining a pre-trained model's knowledge by updating its weights based on domain or task-specific data. It's akin to honing one's skills in a specific field, similar to how one might adapt their quantitative knowledge for machine learning. However, fine-tuning demands technical expertise, encompassing data curation, resource management, and evaluation metrics setup.
To decide between fine-tuning and RAG, AI leaders should consider factors like the need for deep domain understanding, consistent responses, available domain-specific data, and real-time access to knowledge.
Fine-tuning is ideal for specific domain expertise and predictable behavior, while RAG shines in applications requiring up-to-date information and broad, adaptable responses.
RAG offers a cost-effective and scalable solution without the need for constant retraining.
The Crucial Role Of Data Quality In RAG Systems
Successful Retrieval Augmented Generation depends on its integration with external data based on user queries. The quality of data in the external repository is paramount and directly affecting RAG's response accuracy.
» To get accurate responses, you need accurate data. High-quality, accurate data is going to allow your language model to generate more reliable and accurate responses. If that external data is outdated or inaccurate, then the LLM's generation is going to reflect that. «
— Harpreet Sabota
Efficiency and speed are also critical aspects influenced by data quality. Clean, well-structured data leads to quicker retrieval and indexing, resulting in lower latency, faster response times, and reduced computational overhead.
Trustworthiness of external data is paramount, directly impacting the trustworthiness of the generated responses. Data freshness is equally vital to keep the external database up to date.
Understanding The Essence Of RAG
RAG provides a language model with access to real-time data from external databases during inference, expanding its capabilities beyond pre-trained knowledge.
Understanding the advantages of RAG, such as cost efficiency and reduced technical complexity compared to fine-tuning, is crucial.
Leaders must also strategize the implementation of RAG, leveraging its balance between real-time data access and computational efficiency while ensuring robust data infrastructure.
Summary
There are two critical concepts for providing an LLM with new, contextual data: fine-tuning and Retrieval Augmented Generation. Fine-tuning involves adapting a pre-trained model to specific domains, while RAG integrates real-time external data for responses. Fine-tuning requires technical expertise and meticulous data preparation, whereas RAG focuses on accessing up-to-date knowledge. Data quality is pivotal for both, impacting accuracy and relevance. Leaders contemplating these approaches must consider system design, cost-efficiency, data security, and technical complexity.
What are your thoughts on the cost-efficiency of using RAG over fine-tuning?
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