When integrating an LLM into your applicaton to extend it and make it smarter, it’s important to be aware of the pitfalls and best practices you need to follow to avoid some common problems and integrate them successfully. This article will guide you through some key best practices that I’ve come across. Understanding the Challenges of Implementing LLMs in Real-World Applications One of the first challenges is that LLMs are constantly being improved.

Check out the FREE non-technical guide for using AI in your business here: https://clickhubspot.com/fuu7 This video imma be yapping about why prompt engineering is unreasonable and everything that is a bit sus about it. The sequel of this video will probably be me roasting o1 (hopefully not) check out my newsletter https://mail.bycloud.ai/ Special thanks to LDJ for helping with this video OpenAI o1 [Announcement] https://openai.com/index/introducing-openai-o1-preview/ [Blogs] https://openai.com/o1/ Claude 3.5 Sonnet [Announcement] https://www.anthropic.com/news/claude-3-5-sonnet [AntThinking] https://gist.github.com/dedlim/6bf6d81f77c19e20cd40594aa09e3ecd Language Models (Mostly) Know What They Know [Paper] https://arxiv.org/abs/2207.05221 Plan-and-Solve Prompting [Paper] https://arxiv.org/abs/2305.04091 Llama-3.1 System Card [Documentation] https://www.llama.com/docs/model-cards-and-prompt-formats/meta-llama-3/ Think Before You Speak [Paper] https://arxiv.org/abs/2310.02226 Future Lens [Paper] https://arxiv.org/abs/2311.04897 This video is supported by the kind Patrons & YouTube Members: 🙏Andrew Lescelius, alex j, Chris LeDoux, Alex Maurice, Miguilim, Deagan, FiFaŁ, Robert Zawiasa, Owen Ingraham, Daddy Wen, Tony Jimenez, Panther Modern, Jake Disco, Demilson Quintao, Penumbraa, Shuhong Chen, Hongbo Men, happi nyuu nyaa, Carol Lo, Mose Sakashita, Miguel, Bandera, Gennaro Schiano, gunwoo, Ravid Freedman, Mert Seftali, Mrityunjay, Richárd Nagyfi, Timo Steiner, Henrik G Sundt, projectAnthony, Brigham Hall, Kyle Hudson, Kalila, Jef Come, Jvari Williams, Tien Tien, BIll Mangrum, owned, Janne Kytölä, SO, Richárd Nagyfi, Hector, Drexon, Claxvii 177th, Inferencer, Michael Brenner, Akkusativ, Oleg Wock, FantomBloth, Thipok Tham, Clayton Ford, Theo, Handenon, Diego Silva, mayssam [Discord] https://discord.gg/NhJZGtH [Twitter] https://twitter.com/bycloudai [Patreon] https://www.patreon.com/bycloud [Music 1] Spirit Blossom - RomanBelov [Music 2] Tie Me Down - PremiumMusicOdyssey [Music 3] Dimmed Lights - PremiumMusicOdyssey [Music 4] Wrapped in your Love - PremiumMusicOdyssey [Profile & Banner Art] https://twitter.com/pygm7 [Video Editor] @bhargavesque 0:00 Intro 4:21 The prooompting basics 6:36 How companies make prompting works 12:11 Does how you prompt REALLY matter?

What’s the *right* use for AI? Laurie Voss thinks it’s great at doing boring chores, and in this episode we learn what that means and how we can put the robo...

Like Claude Artifacts, but with a backend and database

Welcome to LLM University, your premier learning destination for mastering Enterprise AI technologies. Designed for developers and technical professionals, our hub offers comprehensive resources, expert-led courses, and step-by-step guides to help you start building quickly and stay ahead in the rapidly evolving AI landscape.

Part 1 of the LLM University module on Retrieval-Augmented Generation.

Toy implementation of Strawberry . Contribute to ack-sec/toyberry development by creating an account on GitHub.

Optimizing inference proxy for LLMs.

Code examples and jupyter notebooks for the Cohere Platform - cohere-ai/notebooks

Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model.

LLM training in simple, raw C/CUDA.

Tutorials on tinygrad

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A collection of notebooks/recipes showcasing some fun and effective ways of using Claude. - anthropics/anthropic-cookbook

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An informal capture from the CUDA mode hackathon today.https://github.com/karpathy/llm.c

OpenAI released two major new preview models today: o1-preview and o1-mini (that mini one is not a preview)—previously rumored as having the codename “strawberry”. There’s a lot to understand about …

A blazing-fast semantic search React component. Match by meaning, not just by letters. Search as you type without waiting (no debounce needed). Rank by cosine similarity. - Mihaiii/semantic-autocom...

🚀 https://neetcode.io/ - A better way to prepare for Coding Interviews🧑‍💼 LinkedIn: https://www.linkedin.com/in/navdeep-singh-3aaa14161/🐦 Twitter: https:...

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Practical RAG techniques for engineers: learn production-ready solutions from industry experts to optimize performance, cut costs, and enhance the accuracy and relevance of your applications.

Kong AI Gateway 3.8 includes a new class of intelligent semantic plugins, new advanced load balancing capabilities for LLMs, and the official support for AWS Bedrock and GCP Vertex, in addition to all the other supported LLM providers.

The team behind Artifacts - an innovative new way to interact with Claude - shares how they built this innovative feature in just three months with a distributed team. Exclusive details.

To automatically determine the most important context from your codebase, we've just released v1 of Continue's codebase retrieval feature. You can ask questions like “where do I add a new endpoint to the server?”, “what scripts are used to build this project?”, or “where do I edit the user profile

This week, we discover the different types of bugs that LLM-generated code can provide, and how to mitigate their potential impact.

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When you’re writing in your favorite editor, the AI-like copilot will instantly guess and complete based on what you’ve written in the…

A Comprehensive Overview of Prompt Engineering

Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.

However, there are RAG challenges associated with the process. In this blog, we will explore the key RAG challenges in building LLM applications.