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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LLM Implementation

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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LLM Research

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.

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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: June 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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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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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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