Aditya Gupta

Up to this point, we have focused on AI that handles text: your code, your documentation, and your written prompts. Multi-modality is the revolutionary concept where a single AI model can understand and reason across multiple types of data simultaneously,…

You have built a powerful AI assistant that can understand your code using RAG (Article #1), reason through bugs using Chain-of-Thought (Article #3), and even write autonomous commits (Article #4). But if your AI code review takes 30 seconds to…

When specializing a Large Language Model (LLM) for software development, you are not just teaching it new code, you are teaching it how to behave like a useful assistant. This article clarifies the distinction between the two major specialization methods,…

If you want to customize an open-source Large Language Model (LLM) to truly understand your company’s unique coding style, project structure, or internal terminology, standard fine-tuning is often too expensive and resource-heavy. This is where LoRA comes in. LoRA fine-tuning…

As a developer, you use tools to complete multi-step tasks. An agentic workflow takes this idea and applies it to AI, giving a Large Language Model (LLM) the ability to think, plan, use external tools, and execute complex, multi-step tasks…

The first step on your journey from zero to AI hero is understanding a core concept called Retrieval-Augmented Generation or RAG. If you want to use powerful Large Language Models (LLMs) like those powering GitHub Copilot to work with your…