everything about adopting open source code and models for your llm app
build trust with users and stakeholders - transparency and explainability, reliability and consistency (how to fail safely), automation vs user control. how to evaluate with uncertainty, prompt engineering, effective bug reports
examples and practices of advanced agents, and use of LangGraph for effective tool usage by agents
how to solve latency issues with minimal compromise on quality and cost
The 12 most common challenges around building an effective RAG pipeline and the best practices for solutions
how to define effective requirements for llm apps, and first steps after going to production
What are transformers, why it is so expensive to train a Transformer-based model and what is the architecture of the future LLMs
Indexing knowledgebases with KG for RAG applications
ragas framework for evaluating unstructured retrievals and generations
Evaluating LLMs and AI pipeline in dev and production environments. How to work with datasets
multimodality is a type of model that can analyze multiple data types like language, images, voice, etc.
Find the most suitable project for volunteer support "Iron swords" war efforts
Advanced prompt engineering techniques: skeleton of thoughts, directional stimulus prompting, graph of thoughts, augmentations
Full stack LLM app development, Typescript vs Python
Finetuning techniques (SFT, RLHF...), pros & cons, tools (technical episode)
why should you care about open source models, when and how to finetune, inference best practices, deployment & serving tools, models overview