AI Resources
Practical guides, articles and tools for your AI journey
Knowledge Center
We've compiled a collection of resources to help you navigate the complex world of AI implementation. From technical guides to strategic insights, these materials reflect our practical approach to delivering business value through AI.
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Beyond the Algorithm
Mindset Shift Leads to Success in the Current AI-Driven Job Market
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How to make AI perform better?
Maybe take a few minutes to think about your prompt?
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Why should you be careful with AI?
How does Duolingo’s "failure" on AI strategies inspire you?
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Do you need 700 engineers to make money with AI?
How to make impact with AI?
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Beyond Basic RAG
Building Stateful, Agentic Retrieval Systems with LangGraph
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Beyond ChatGPT: Enterprise LLM Integration Best Practices
Practical strategies for moving beyond simple ChatGPT usage to sophisticated enterprise LLM applications. Learn how to address common challenges like context management, security, and evaluating outputs at scale.
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Unlocking Deeper Reasoning in LLMs: Introducing Atom of Thought (AoT)
Large Language Models (LLMs) have made impressive strides in understanding and generating text. Yet, when it comes to tackling complex, multi-step problems, traditional prompting methods like Chain-of-Thought (CoT) can fall short.
Read on Medium →Books by Our Team
Our comprehensive training programs are built on practical implementation experience featured in bestselling books authored by our team
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:
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:
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.
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