Aditya Gupta

As developers integrate AI deeply into their workflows, moving toward autonomous tools and code generation, the risk of the AI producing harmful, insecure, or policy-violating code increases. Guardrails are the non-negotiable safety rules and checks that ensure the AI system…

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…

When you use an AI assistant, the way you ask the question, known as prompting, drastically affects the quality of the answer. For developers, this is the difference between getting a working solution to a complex bug and getting generic…

The foundation of modern AI developer tools, especially those that use RAG (Retrieval-Augmented Generation), is a seemingly magical concept: code embeddings. If you want to understand how code embeddings work for search within your project, this article is essential. Embeddings…

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…