Chelsea AI Ventures

AI Resources

Practical guides, articles and tools for your AI journey

We publish on the Chelsea AI newsletter, on what is working in production AI and what is not. It is where we named the Stochastic Man-Month Rule: adding AI to a workflow amplifies the verification workload faster than it speeds delivery. Articles, podcast appearances and recorded sessions are collected below, each with a summary so you can tell whether it is worth your time. Then the books our director has written, and the talks we have given.

Browse by topic

Four threads run through everything we publish. Each one collects the articles behind a part of what we do.

Books by Our Team

Our training programs are built on practical implementation experience featured in bestselling books authored by our team

Cover of Retrieval Augmented Generation, The Seminal Papers

Retrieval Augmented Generation, The Seminal Papers

Published: March 2026 | New Release

Principles for architecting reliable and verifiable AI

Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data. RAG has grown from a simple prompt engineering workflow into a sophisticated set of data analysis, storage, and retrieval techniques. Retrieval Augmented Generation, The Seminal Papers explores foundational research papers that explain why RAG works, how it’s built, and what makes it different from other approaches.

Focus areas include:

Retrieval Augmented Generation AI Architecture Verifiable AI
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Cover of Generative AI with LangChain

Generative AI with LangChain (2nd edition)

Published: May 2025 | Amazon Bestseller in Programming

Build production ready LLM applications and advanced agents using Python and LangGraph

A practical guide to leveraging LangChain and LangGraph for GenAI implementation, with real-world examples ranging from customer support to data analysis. The 2025 edition features updated code examples and improved GitHub repository.

Focus areas include:

Enterprise-grade LLM application architecture Prompt engineering best practices RAG implementation for knowledge augmentation Custom agent development Production deployment strategies
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Cover of Machine Learning for Time Series

Machine Learning for Time Series

Published: October 2021 | Industry Standard Reference

Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods. This comprehensive guide covers everything from data preprocessing to advanced models for time-dependent data. The included tutorials range from simple forecasting to complex deep learning architectures for time series analysis.

Focus areas include:

Anomaly detection systems Forecasting methodologies Feature engineering for time-series Deep learning approaches Production deployment patterns Time series preprocessing techniques LSTM and RNN architectures
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Cover of Artificial Intelligence with Python Cookbook

Artificial Intelligence with Python Cookbook

Published: October 2020 | BookAuthority Best-Seller

Proven recipes for applying AI algorithms and deep learning techniques

Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch. The practical cookbook approach provides ready-to-use solutions for common AI challenges, from computer vision to natural language processing, with complete code examples and detailed explanations of implementation considerations.

Focus areas include:

Deep learning fundamentals Computer vision applications NLP implementation techniques Reinforcement learning Model optimization strategies Transfer learning approaches Hyperparameter tuning
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Speaking & Education

The GenAI Build Lab: Build Production-Ready RAG with Open Models

Packt Publishing, 2026

Focus: Production RAG on small open models: hybrid retrieval and reranking, corrective retrieval, RAGAS evaluation, and open guardrail models

Designing secure, customised, and sovereign LLM workflows

DataFest Tbilisi 2025

Focus: Workshop on cascaded agentic pipelines, grammar-enforced structured output, guardrails and golden-set evaluation on local CPU

See the event page

Why open-source LLMs are the future of enterprise AI

DataFest Tbilisi 2025

Focus: Moving from public APIs to on-premise open-source models, and where small language models fit

See the event page

Moving Beyond Statistical Parrots: LLMs and Their Tooling

Put Generative AI to Work 2023, ODSC 2024, Data Science Week Amsterdam

Focus: Open-source LLMs and enterprise implementation

Future of Data Science and LLMs

PyData London 2023

Focus: Panel discussion on open-source AI technologies

Experimentation at Scale at loveholidays

RE•WORK, 2023

Focus: Running dozens of concurrent A/B tests on an in-house platform, at low bias and reduced lag

Watch the recording

Mastering Machine Learning Techniques for Time-Series Analysis

PyData Bristol 2023

Focus: Feature extraction, forecasting and anomaly detection for time series in Python

View the slides

Time-Series Development

University of Oxford 2023

Focus: Guest lecture on the Artificial Intelligence: Cloud and Edge Implementations course

Strategic AI Implementation

Data & Analytics Summit EU 2022

Focus: Enterprise AI strategy and implementation roadmaps

Time-Series in Python: Preprocessing and ML

ODSC 2022

Focus: Machine learning techniques for time-series data

ML: The Good, The Bad and the Ugly

DataFest Tbilisi 2019

Focus: Where machine learning delivers in production and where it goes wrong

Podcast appearances:

Looking for a speaker for a conference, an internal session or a podcast? We take a few each year, on generative AI, MLOps and time series. Get in touch