Clarifai’s Post

In this blog post, we’ll review the different strategies to work with LLMs, and take a deeper look at the easiest and most commonly used option: using existing LLMs through APIs.

Good reading for building successful products around #LLM, drawing from experiences and pointing to examples from around the industry. It emphasizes the importance of crafting prompts carefully, breaking complex tasks into simpler steps, and optimizing the context provided to LLMs. For prompting, the authors recommend splitting prompts into focused components that are easy to understand and iterate on individually. They advise carefully considering the context needed and structuring it effectively to highlight relationships between parts. Retrieval-augmented generation (#RAG) is highlighted as an effective way to provide relevant knowledge to LLMs. The quality of RAG output depends on the relevance, density, and detail of the retrieved documents. The authors suggest using a hybrid approach combining keyword search and embedding-based retrieval. They argue that RAG may be preferable to fine-tuning for incorporating new knowledge into LLMs. While long context windows like Gemini's 10M tokens may reduce the need for RAG in some cases, the authors contend that RAG will remain useful for selecting relevant information and avoiding overwhelming models with distractors. The article recommends using multi-step "flows" to break down complex tasks, caching responses to save costs and ensure consistency, and fine-tuning models when prompting falls short but only if the upfront cost is justified. For evaluation and monitoring, the authors advise creating assertion-based unit tests from real samples, simplifying annotation tasks to binary decisions or pairwise comparisons, and considering reference-free evaluations as potential guardrails. They note that LLMs may generate output even when they shouldn't, necessitating careful monitoring for factual inconsistencies. https://lnkd.in/eBmaKJ28

Interesting article by Tomaz!

Take Tomaz Bratanic's advice: "By splitting longer documents into smaller vectors and indexing these for similarity, we can increase the retrieval accuracy while retaining the contextual information of parent documents to generate the answers with LLMs." Learn how in his article about implementing Advanced Retrieval RAG Strategies With #Neo4j ⤵️ https://bit.ly/3t98kVY #LLMS #GenAI #RAG #Vector

Great article by O'Reilly! Here are some key takeaways: Utility and Limitations: LLMs are powerful tools capable of handling various tasks, from generating text to answering questions and providing recommendations. However, they also have limitations, such as the inability to understand context deeply and the tendency to produce plausible-sounding but incorrect or nonsensical answers. Human-AI Collaboration: One significant insight is the importance of integrating human oversight in the loop. Combining human expertise with LLM outputs can enhance the overall performance and reliability of the system. Human review and intervention are crucial in ensuring accuracy and mitigating errors. Prompt Engineering: Crafting effective prompts is essential for extracting the best performance from LLMs. This process, known as prompt engineering, involves iteratively refining input prompts to improve the quality and relevance of the model's responses. Ethical and Practical Challenges: Building with LLMs involves addressing ethical concerns such as bias, misinformation, and data privacy. It is vital to implement measures to handle these issues responsibly. Additionally, practical challenges include ensuring scalability and managing computational costs. Future Directions: The article suggests that ongoing improvements in LLM technology, combined with better tools for integration and management, will continue to expand their applications. Areas like personalized education, advanced customer support, and creative content generation are expected to see significant advancements. For more detailed information, you can read the full article on O'Reilly Radar's website. (https://lnkd.in/eNuYDpP7)

This is what I have been thinking: How do you tokenize a knowledge graph? Even more precise, how do you tokenize a standards based knowledge graph? https://lnkd.in/gAgq7-Qw

Retrieval augmented generation: Keeping LLMs relevant and current - Stack Overflow https://lnkd.in/dtwwuhCb

🧠 LLMs revolutionize text-based interactions, aiding in summarization, explanation, question answering, and code generation. BE-COME https://lnkd.in/dqB8PwgK

How would you feel if you found a way to make your LLM more context aware? Amazing, right? Here's an article explaining the concept of RAG-fusion! A technique that aims to improve the retrieval output and enhance your LLM's output: https://lnkd.in/d4E4uhE4 #largelanguagemodels #searchengine #vectorsearch #machinelearning

Providing examples is a useful tool for prompting LLMs, but did you know that the specific choice of examples has a dramatic impact on performance? Our own Lucy Cheng made a guide on how to pick examples to get the highest accuracy... https://lnkd.in/gqwxhjZr