Guide to creating your own B2B and B2C AI

Discover the world’s first guide to creating your own artificial intelligence tailored to your specific use case. This 13-episode series will guide you step by step in creating your own proprietary AI, with a new tutorial released each week, covering all the essential aspects to turn your vision into reality.

Episode 1: What type of AI?

This first episode aims to help you identify the type of model you need (automation, interaction, or correction). Then, understand the necessary components and strategies, such as prompt-tuning, fine-tuning with LoRA, and reinforcement learning techniques like RL and DPO, to build and optimize your AI model. Finally, agents play a crucial role in automating tasks or correcting system anomalies.

Episode 2: What AI architecture?

The episode 2 outlines key considerations for building a deep learning model, starting with understanding whether you’re working from scratch or within an existing environment. Important factors include domain expertise, data type, data streaming needs, software integration, output dynamics, performance requirements, regulatory compliance, interpretability, scalability, and computational resources.

Episode 3: How to optimize prompt engineering?

In creating your own model and mastering prompt engineering, context is the true strength. It’s not the only technique, but it’s a fine-tuning adjustment.

Episode 4: What is dynamic prompting, and how does it work?

Is it possible to add a variable in a language model?
Yes, it is possible. Sometimes, it is necessary to add a variable to meet a specific need.

Episode 5: Which types of databases?

It is important to choose the right database for your LLM. Here, you can see the Knowledge Graph, effective for complex relationships, and RAG, which combines information retrieval with text generation. The choice depends on the usage context.

Each episode of this guide will bring you closer to creating a fully customized and efficient AI, tailored to your specific needs. Follow the links to explore each step in detail and build an AI that will transform your business.

Episode 6: Indexing and Clustering - Fundamental Concepts for Efficient Data Retrieval

Episode 7: Storage and retrieval in knowledge graphs

Episode 8: How to retrieve context using RAG & Chroma DB

Learn how to optimize contextual data retrieval with Retrieval-Augmented Generation (RAG) and Chroma DB. Vector databases offer enhanced performance, particularly for complex similarity searches, outperforming traditional solutions like Neo4j. With indexing algorithms such as FAISS and Chroma DB’s compatibility with technologies like NumPy and LangChain, you can ensure fast and accurate results, even at scale. Follow our Golden Rule: index for complex retrievals or large databases.

Episode 9: LLM with agent, backend or both

Agents connected to LLMs can automate tasks and exhibit advanced features like autonomy and learning. By combining LLMs with agents and backends, businesses can create scalable and efficient AI solutions for complex use cases. However, managing multi-agent environments can require significant computational resources.

Episode 10: Fine-tuning and generalization

In modern business environments, achieving generalization in language models (LLMs) is essential for adapting to diverse tasks. LoRa (Low-Rank Adaptation) offers a quick proof of concept but falls short in complex scenarios. To address this, advanced methods like Direct Preference Optimization (DPO), Reinforcement Learning with Human Feedback (RLHF), and Reinforcement Learning with Machine Feedback (RLMF) directly modify the neural network, enabling deeper learning and better adaptability. RLMF, a dual-model approach, uses machine-generated feedback to refine responses, making it more efficient and scalable for complex business applications.

Episode 11: Test "Generalization" for creating a Business Domain Expert with LLMs

This post covers the final steps in fine-tuning a large language model (LLM) for generalization, emphasizing the importance of achieving 90% accuracy through Direct Preference Optimization (DPO), data understanding, and explanation tuning. It highlights how explanation tuning improves model robustness and interpretability, though generalization relies more on diverse training data. Following these steps ensures a more effective and structured model training process.

Episode 12: AI training AI - RLMF

Reinforcement Learning Machine Feedback (RLMF) enhances large language models by using external data for real-time self-correction and optimization. This innovative approach leverages feedback loops to improve LLM accuracy and adaptability over time, surpassing traditional methods like Google’s SCoRe and Meta’s Mixture of Judges.

Episode 13: Chain of Tasks

The Chain of Tasks automates task execution without focusing on reasoning, prioritizing their completion. Each step builds on the outcome of the previous one. It is also important to follow certain best practices to fine-tune the models and manage errors in multi-step processes.

Each episode of this guide will bring you closer to creating a fully customized and efficient AI tailored to your specific needs. Follow the links to explore each step in detail and build an AI that will transform your business.