educative.blog
For developers, by developers
Trending
What are the common challenges in implementing RAG?
Retrieval-augmented generation (RAG) is one of the most popular architectural patterns in modern LLM applications. It promises better factual grounding, reduced hallucinations, and dynamic knowledge integration. But like any pattern that looks simple on paper, there’s a great deal of complexity hiding under the hood. This blog breaks down the most common RAG challenges developers face in real-world implementations and how to navigate them like a pro.
Zach Milkis
Jul 1 · 2025
What benefits does LangChain offer to developers?
LangChain empowers developers to move beyond simple LLM prompts and build modular, production-ready AI apps. With features like memory, tool integration, RAG support, and agent workflows, it simplifies prototyping and scales to complex use cases. Whether you're creating chatbots or research tools, LangChain makes AI development faster, cleaner, and more flexible.
Sumit Mehrotra
Jun 30 · 2025
12 real-world LangChain usecases
LangChain enables developers to build powerful LLM applications across real-world scenarios, from chatbots with memory and private document Q&A to research bots, legal summarization, and workflow automation. This guide explores 12 LangChain usecases that demonstrate how developers can move beyond basic prompts into production-ready systems.
Mishayl Hanan
Jun 30 · 2025
What are entry-level prompt engineer jobs? A beginner’s guide
Prompt engineering is a fast-growing GenAI role, and you don’t need deep ML chops to break in. This guide shows where entry-level jobs hide, like under titles like AI Content Specialist or LLM Ops Associate, what daily work looks like, and which skills matter most: clear writing, structured thinking, and relentless testing. Discover hiring sectors, smart search tactics, and how to craft a portfolio that proves you can talk to models.
Areeba Haider
Jun 30 · 2025
Top 5 Best Generative AI Courses
Everyone wants to build with generative AI, but figuring out where to start? That’s where most developers get stuck. The field moves fast, and many tutorials are either too shallow, outdated, or assume you’ve already trained a transformer from scratch. This blog highlights the best generative AI courses for developers who want to build real tools, apps, and agents.
Khayyam Hashmi
Jun 30 · 2025
What Are the Steps Involved in Fine-Tuning a Language Model?
In this blog, we’ll walk through the key steps involved in fine-tuning a language model, highlighting tools, best practices, and pitfalls to avoid.
Areeba Haider
Jun 27 · 2025
What Tools Are Used to Fine-Tune LLMs?
Fine-tuning isn’t limited to picking a model and training it. It’s a multi-stage process involving data prep, training frameworks, evaluation, and deployment. In this blog, we’ll walk through the core categories of tools used to fine-tune LLMs — and why they matter.
Zarish Khalid
Jun 27 · 2025
Should You Prompt or Fine-Tune Your Language Model?
In this blog, we’ll explore the trade-offs between prompt engineering vs fine tuning LLMs, and help you understand when it’s worth moving beyond zero-shot prompts to custom model training.
Sumit Mehrotra
Jun 27 · 2025
What tools are commonly used in RAG systems?
RAG, or Retrieval-Augmented Generation, has quickly become a default architecture for building intelligent, grounded LLM applications. But while the pattern sounds simple (“retrieve then generate”), real-world systems require a stack of carefully chosen tools. The right components can mean the difference between brittle hacks and production-grade intelligence. In this blog, we’ll break down the core categories of RAG tools, highlight popular choices, and show how each piece fits into the end-to-end pipeline.
Zach Milkis
Jun 26 · 2025