Tutorials on Ai

Explore effective recovery strategies for multi-agent LLM systems to minimize downtime and enhance operational resilience.

Explore the essentials of scalable benchmarking for large language models, revealing techniques, tools, and best practices for effective evaluation.

Explore how Retrieval Augmented Generation (RAG) enhances AI efficiency, tackles bottlenecks, and the importance of hands-on training for long-term success.

Explore essential strategies for securing AI workflows against data breaches, insider threats, and compliance challenges in a rapidly evolving landscape.

Learn how to select the best automation frameworks for LLM pipelines, focusing on compatibility, integration, scalability, and monitoring features.

Learn how to effectively monitor large language models for reliability, bias prevention, and compliance through interpretability techniques.

Ensure compliance with LLM regulations in government by following this comprehensive checklist to mitigate risks and uphold ethical standards.

Explore essential load balancing techniques for optimizing large language model pipelines, enhancing performance and resource efficiency.

Learn how to deploy large language models on AWS Lambda with serverless hosting, cost-saving strategies, and performance optimization techniques.

Hello and welcome on Part 3 of our tutorial on how to build sophisticated full stack applications with Bolt , UX Pilot , Supabase , and ChatGPT . In Part 1 of our tutorial, we set the stage and created a plan for our app, including our Phase 1 (aka detailed MVP ) features, a UI Development Plan , and a Design System we can use to create a beautiful app of this kind. In Part 2 , we took this plan, put it to good use, and created an actual High Fidelity design for each and every part and each and every screen of our app.

Hello and welcome to Part 2 of our tutorial about how to build actually professional fullstack applications with AI. In Part 1 of our tutorial, we set the stage and created a plan for our app, including our Phase 1 (aka detailed MVP) features, a UI Development Plan , and a Design System we can use to create a beautiful app of this kind. Today, we’ll see how we can take this plan, put it to good use, and create an actual High Fidelity design for our app, which we are then going to use to build the app itself - but that will be on Part 3.

When was the last time you went to church? And how often do you go? If you’re anything like the vast majority of people, the answer is probably “not very often, if at all”

Explore effective strategies to optimize GPU costs for fine-tuning large language models without compromising performance.

Explore how dynamic prompts using LangChain templates enhance AI interactions, improving efficiency and user satisfaction across applications.

Learn how knowledge distillation transforms large AI models into smaller, efficient versions without sacrificing performance, enabling broader deployment.

Learn how fine-tuning large language models on imbalanced customer support data can enhance performance, accuracy, and customer satisfaction.

Explore the complexities of training multilingual LLMs using diverse support data, addressing challenges and strategies for effective deployment.

Learn how to effectively benchmark large language models using open-source libraries, focusing on key metrics and best practices for evaluation.

Learn how quantization enhances transformer models for edge devices, improving efficiency, speed, and accuracy while reducing resource demands.

Learn essential strategies for optimizing, automating, and monitoring transformer models on edge devices to enhance performance and reliability.

Learn how to build cost-efficient data pipelines for large language models through strategic planning and optimization techniques.

Explore how automated prompt testing revolutionizes AI development, enhancing efficiency, accuracy, and performance through advanced methodologies.

Explore how the Model Context Protocol (MCP) is revolutionizing enterprise AI integration, enhancing security, and improving operational efficiency.

Explore the differences between 4-bit and 8-bit quantization in AI, focusing on memory efficiency, speed, and accuracy trade-offs.

Explore essential steps in building efficient data pipelines for large language models, from data collection to automation and optimization.

Explore the origins of bias in large language models and effective strategies to mitigate its harmful effects in AI applications.

As you may know, lately we’ve created a lot of tutorials on how to build with AI or applications that use AI or both. If you didn’t know, well, we’ve already created apps with Bolt , Replit Agent , Supabase , MCP Servers , Chatbots , and more. You can find them here (and filter for the keyword or tool you want to read about). But one thing we haven’t done so far, is show you how to incorporate AI in more complicated features, like media handling (images, voice, etc).

Explore how fine-tuning Large Language Models can enhance data generation for rare events, improving predictive accuracy and model performance.

Learn how to safeguard LLM pipelines from adversarial attacks with effective strategies and solutions to protect sensitive data.

Explore the seven essential metrics for evaluating AI agents, focusing on efficiency, reliability, and adaptability in diverse applications.