WHY Retrieval Augmented Generation (RAG) is OVERRATED!
TLDRThe video discusses the limitations of Retrieval Augmented Generation (RAG) in production environments. It argues that despite its promise in addressing hallucinations in large language models, RAG often falls short and does not eliminate the issue entirely. The speaker shares experiences from various industries, highlighting the technical challenges and high costs associated with RAG implementations. They suggest that advancements in hardware costs and language model capabilities are necessary for RAG to become a viable solution for real-world applications.
Takeaways
- 🚫 Retrieval Augmented Generation (RAG) is currently overhyped and not effective for most production use cases.
- 💡 RAG was initially designed to address the issue of hallucinations in large language models, but it doesn't completely solve the problem.
- 🛠️ Developing a RAG prototype is easy due to available tools like Lang chain and vector databases, leading to misconceptions about its applicability.
- ⚖️ Large AI companies with substantial funding are the only ones to successfully implement RAG, leveraging their resources.
- 📈 RAG's effectiveness decreases with the number of queries, as hallucinations become more frequent over time.
- 📚 The structure and format of documents used in RAG can greatly impact the accuracy of the retrieval process.
- 💸 RAG implementations are often costly and can lead to financial difficulties for companies that underestimate the expenses.
- 💻 High hardware costs contribute to the prohibitive expense of RAG, with a reliance on specific GPUs and computing platforms.
- 📈 For RAG to be practical, hardware costs need to decrease, and language model capabilities must improve.
- 🔄 Training large language models specifically for RAG tasks could enhance performance and reduce the occurrence of hallucinations.
- ⚠️ It is advised to not solely rely on RAG for production and to consider additional verification measures for its outputs.
Q & A
What is Retrieval Augmented Generation (RAG) and why is it considered overrated?
-Retrieval Augmented Generation (RAG) is a method that combines large language models with a retrieval system to provide more accurate and contextual responses. It is considered overrated because, despite its promise to reduce hallucinations in large language models, it does not eliminate them entirely and may still produce incorrect or hallucinated responses.
What was RAG initially designed to address?
-RAG was initially designed to address the problem of hallucinations with large language models by retrieving relevant context to provide a more accurate response.
Why do some clients struggle to apply RAG to their domain?
-Some clients struggle to apply RAG to their domain because they may have been misled by the ease of developing a prototype or proof of concept, but the actual implementation in their specific domain can be more complex and may not yield the expected results.
What are the challenges in the retrieval aspect of RAG in production use cases?
-The challenges in the retrieval aspect of RAG in production use cases include dealing with non-uniform documents, determining the optimal chunk size for various document types, and keeping up with constant changes in the documents that affect the retrieval process.
How does the structure and form of documents impact RAG?
-The structure and form of documents impact RAG because they determine how the documents should be chunked for the vector database. Different types of documents, such as menus, about pages, or service descriptions, require different chunking strategies, which affects the accuracy of the retrieved information.
What is a common issue with RAG implementations in production?
-A common issue with RAG implementations in production is the cost. RAG can be prohibitively expensive due to the increased number of input tokens that need to be processed by the large language model, leading to higher costs for both memory and computation.
What are the two main factors that need to improve for RAG to be more practical in production?
-The two main factors that need to improve for RAG to be more practical in production are the cost of hardware, which would reduce the cost of queries to large language models, and the capabilities of language models themselves, which could be enhanced through more powerful models or specific training for RAG tasks.
Why might RAG not be suitable for production use cases that require high accuracy?
-RAG might not be suitable for production use cases that require high accuracy because it can still produce hallucinations and incorrect responses. Additionally, the cost and complexity of implementing RAG can be prohibitive for many use cases where mistakes cannot be tolerated.
What is the role of the prompt in RAG and how can it lead to contradictions?
-The prompt in RAG is used to guide the language model to answer based on the retrieved context. However, sometimes the model might not follow the prompt correctly and can produce responses based on the weights it was trained on, leading to contradictions between the retrieved context and the generated response.
How does the cost of RAG affect product development?
-The cost of RAG can significantly affect product development as it can lead to high expenses due to the increased number of input tokens processed by the large language model. This can result in a high burn rate for companies, making it difficult to sustain the product in the long term.
What are some suggestions to improve the effectiveness of RAG?
-Suggestions to improve the effectiveness of RAG include training language models specifically for RAG tasks, reducing the cost of hardware to lower query costs, and implementing additional checks or layers to verify the accuracy of the information retrieved and generated by the RAG system.
Outlines
🤖 Hype vs. Reality of Retrieval Augmented Generation (RAG)
The speaker, an AI consultant with years of experience and developer of AI products, asserts that Retrieval Augmented Generation (RAG) is currently mostly hype and not effective for most production use cases. Despite its initial promise of mitigating hallucinations in large language models, RAG has not lived up to expectations. The ease of developing prototypes with tools like Lang chain or Llama index and the availability of vector databases have contributed to the hype. However, the speaker has often had to dissuade clients from implementing RAG due to its limitations. The speaker provides examples from various industries, such as legal and hospitality, where RAG has not performed well. The only successful implementations are by major AI companies with substantial funding. The speaker then delves into the technical reasons behind RAG's shortcomings, particularly its inability to fully eliminate hallucinations.
🧠 Challenges with RAG's Hallucination Problem
The speaker discusses the issue of hallucinations in RAG systems, which are incorrect answers generated by the model despite the correct context being retrieved. This occurs due to contradictions between the model's training data and the retrieved context. The model sometimes defaults to its training data rather than the retrieved context, leading to inaccuracies. The speaker notes that these hallucinations become more frequent with the number of queries, posing a significant problem for production systems that require high accuracy, such as legal applications. The speaker also highlights the limitations of using RAG with smaller open-source models as opposed to more powerful models like GPT-4, which may better adhere to the retrieved context.
📚 Navigating the Retrieval Aspect of RAG in Production
The speaker addresses the challenges of the retrieval aspect of RAG in real-world applications. The difficulty lies in the variability of document formats and structures, which impact the efficiency of the retrieval process. The speaker uses the example of building RAG chatbots for the hospitality industry, where information from diverse sources like hotel websites, menus, and amenities descriptions must be integrated. The structure of these documents, such as short paragraphs in a menu versus longer narrative in an about page, affects the optimal chunk size for retrieval. The speaker also points out that these documents are dynamic, with frequent updates that complicate the retrieval process. Furthermore, the speaker warns of the high costs associated with RAG implementations, which often lead to financial strain due to underestimating the expenses involved.
💸 Understanding the Expensive Nature of RAG
The speaker explains why RAG is expensive for production use cases, focusing on the costs associated with using large language models. The speaker uses a standard chatbot example to illustrate how the cost increases with the length of conversation due to the growing number of input tokens. This cost is further exacerbated in RAG applications because of the additional retrieved context, leading to a larger average token input for the model. The speaker emphasizes that even open-source models incur costs due to the increased memory required to process larger inputs. The speaker advises product managers and developers to consider these costs and the need for high accuracy in their use cases before implementing RAG.
🚧 Path to Practical RAG Implementations
The speaker outlines two key developments needed for RAG to become practical for production use: a reduction in hardware costs and improvements in language model capabilities. The speaker suggests that more affordable hardware would lower the costs of queries to large language models, making RAG more economically viable. Additionally, the speaker proposes training models specifically for RAG tasks to improve performance and potentially eliminate initial hallucination issues. Until these advancements are realized, the speaker advises against over-investment in RAG and recommends considering alternative approaches or adding verification layers to mitigate risks.
Mindmap
Keywords
💡Retrieval Augmented Generation (RAG)
💡Hype
💡Hallucinations
💡Prototypes
💡Use Cases
💡Large Language Models
💡Retrieval Process
💡Cost
💡Hardware
💡Language Model Capabilities
💡Production
Highlights
Retrieval Augmented Generation (RAG) is currently overhyped and not effective for most production use cases.
RAG was initially designed to address the issue of hallucinations in large language models.
RAG's ease of prototype development contributes to its widespread hype.
Many clients have approached the speaker with RAG use cases, often leading to disappointment due to its limitations.
Large AI companies like OpenAI have successfully implemented RAG due to their extensive funding.
RAG does not completely eliminate hallucinations, leading to incorrect responses.
The contradiction between the model's training data and retrieved context can result in hallucinated answers.
RAG's retrieval aspect is challenging in production due to the non-uniformity of information sources.
The form and structure of documents impact the optimal chunk size for effective retrieval in RAG.
RAG implementations are often costly and can lead to financial difficulties for companies.
The cost of hardware and language model capabilities need to improve for RAG to be practical in production.
Training large language models specifically for RAG tasks might improve their performance and reduce hallucinations.
RAG's increased input token requirements lead to higher costs for production use cases.
The complexity and cost of RAG applications can be mitigated by adding a sense-checking layer or providing source citations.
RAG's prototype ease can be misleading, and its production application should be approached with caution.
For production use cases where accuracy is crucial, alternative approaches should be considered over RAG.
The speaker recommends not getting carried away with RAG and considering the pricing and accuracy requirements of production applications.