LangTalks

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

exploring the reranker component in the 2 stage retrieval system

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

LCEL, LangGraph, LangSmith

Indexing knowledgebases with KG for RAG applications

ragas framework for evaluating unstructured retrievals and generations

Retrieval Augmented Generation process, Llama index vs LangChain, indexing

OpenAI DevDay announcements recap

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

Drill down on a summarization task use case

Save time building applications with LLMs by utilizing tools like LangChain, Llama index, Guidance, GPTCache and many more

Why you might need a VectorDB, which features to consider when choosing a provider, and use cases examples

From an agent POC with LangChain to production challenges

Security, privacy, compliance & safety of LLM-based applications

Explore the theory of embeddings and their practical applications

Overviewing the main concepts of LangChain framework and how to get started

Theory and benchmarks for zero shot, few shot, chain of thoughts, and tree of thoughts

The evolution of Language Models and OpenAI's training techniques.

Introduction about Lee & Gal and what LangTalks is about