RAG vs Large Context Window LLMs: When to use which one?

Large language models (LLMs) are constantly evolving, and one key area of development is how much context they can consider when generating text. Large language models (LLMs) are incredibly powerful tools for processing and generating text. However, they inherently struggle to understand the broader context of information, especially when dealing with lengthy conversations or complex tasks. This is where large context windows and Retrieval-Augmented Generation (RAG) come into play. Both have their advantages and disadvantages, and the best choice for your project depends on your specific needs, but here is why context is needed!

The Need for Context:

Imagine you're having a conversation with someone. To understand their current statement, you need to consider what they've said previously. LLMs without proper context awareness might struggle with this. Here's why context matters:

• Maintaining Coherence: In a conversation, if someone mentions "the dog" later, you understand they're referring to the dog discussed earlier, not a random new dog. Large context windows or RAG help LLMs maintain this coherence across interactions.

• Understanding Complexities: Some tasks require understanding intricate relationships within information. For instance, summarizing a research paper involves grasping connections between methodology and results. A large context window or RAG allows the LLM to consider all relevant sections for a more comprehensive understanding.

• Reducing Hallucinations: When LLMs lack context, they might invent information to fill the gaps, leading to nonsensical outputs. Large context windows or RAG provide more information to ground the LLM's generation in reality.

Large Context Windows

A large context window allows the LLM to process a greater amount of text before generating its response. The LLM can consider a lot of information at once, which helps it understand the bigger picture and generate responses that are more relevant to the overall topic. This can be beneficial for tasks that require a deep understanding of the conversation history or background information. However, processing massive amounts of text is computationally expensive and slow.

Benefits of Caching when using large context window

One way to mitigate the cost of large context windows is by implementing caching. Caching stores previously processed contexts to be reused when similar prompts arise. This can significantly improve response times, especially for repetitive tasks.

• Example: Imagine a large language model used for summarizing research papers. With caching, the LLM can store the processed context (introduction, methodology, etc.) of previously summarized papers. When encountering a new paper with a similar structure, the LLM can reuse the cached context, focusing only on the novel elements (results, conclusion) for summarization.

However, caching introduces additional complexity. You need to determine what information to cache and for how long. Additionally, the effectiveness of caching depends on the predictability of your prompts. If prompts are highly varied, caching may not provide significant benefits.

RAG Retrieval Augmented Generation

RAG is a technique that improves the accuracy and reliability of large language models (LLMs) like GPT-3 and others. It does this by linking the LLM to an external knowledge base (like Wikipedia or a company's internal documents). RAG lets the LLM search for and use relevant information from this knowledge base before generating a response. RAG offers several advantages over even a cached large context window approach:

• Efficiency: RAG retrieves only the most relevant information, making it faster and more cost-effective.

• Accuracy: Focusing on relevant information reduces the risk of hallucinations and improves factual accuracy.

However, RAG offers an alternative approach, but it requires more upfront effort. Setting up a RAG system involves building and maintaining a retrieval system that relies on vector search and embeddings to efficiently find the most relevant information for the LLM to work with.

RAG vs Large Context Window

Large context windows allow the LLM to directly access and process a significant amount of previous information, which is beneficial for complex tasks. However, this approach can be computationally expensive and slow. RAG takes a more targeted approach. It utilizes a retrieval system to find the most relevant pieces of information from a vast knowledge base, feeding them to the LLM. This is faster, more cost-effective, and reduces the risk of errors. However, RAG requires a well-functioning retrieval system and can be more complex to set up initially. Ultimately, the best choice depends on your specific needs, whether you prioritize deep analysis, efficiency, or both.

Here's a breakdown to help you decide:

• Large Context Window with Caching: Choose this if deep analysis of large datasets is needed, and there's some predictability in your prompts for effective caching.

• RAG: Opt for RAG if you prioritize efficiency, factual accuracy, or your prompts are highly varied and caching wouldn't be beneficial.

Ultimately, the best approach depends on your specific project requirements and resource constraints. Consider the trade-offs between cost, accuracy, complexity, and the predictability of your prompts when making your decision. Hopefully this blog helped you understand the difference between RAG and Large Context Window. Subscribe to get notified with my next blog.