Generative AI
Generative AI tutorials, LLMs, ChatGPT, AI agents, RAG, prompt engineering, and practical GenAI guides.
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RAG Chunking Strategies Tested: Fixed vs Recursive vs Semantic Chunking
RAG Chunking Strategies decide what your retriever is allowed to find. If a useful answer is split across two chunks, buried inside a large mixed-topic chunk, or detached from the heading that gives it meaning, a better prompt will not repair the damage. The failure happened before the model saw the question. Choosing the right…
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GLM-5.3 Architecture and API Setup: A Python Walkthrough
By Ravi Kiran · Editorial scope updated 19 October 2026. This article covers architecture and API setup. Cost comparisons and test observations are maintained in the linked companion analysis. GLM-5.3 arrives at a moment when the AI model race is no longer only about who can generate the most convincing paragraph or answer a difficult…
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GLM-5.3 Cost Analysis: Calculator, Token Usage and Workload Trade-offs
By Ravi Kiran · Editorial scope updated 19 October 2026. This analysis focuses on cost per completed task: token rates, cache usage, output length, retries and the limits of benchmark comparisons. Use the calculator to explore a workload, then compare the estimate with actual billed usage. For architecture, provider configuration and a Python API example,…
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DeepSeek V4.1 Flash: What It Costs and When to Use It
DeepSeek V4.1 Flash was released on 10 September 2026. The short version: it is a 552-billion-parameter mixture-of-experts model with native vision, a one-million-token context window, MIT-licensed weights, and pricing low enough to change what is economically sensible to build. This guide sticks to figures published by DeepSeek in its API documentation and model card. Where…
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Why Your RAG Returns Wrong Answers: 8 Failure Modes, Measured
A labelled 28-document corpus and 20 queries, measured. Post-filtering returned zero results for 65% of queries, error codes embedded to a 0.000 vector norm, and an unanswerable query outscored every legitimate one. With the full code.
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LLM System Design Interview: 15 Questions, Answers & a Framework That Works (2026)
There is a specific way people fail this interview, and it has almost nothing to do with how much they know about machine learning. The interviewer says: “Design a customer support assistant powered by an LLM.” The candidate draws a box labelled LLM, connects it to a database, and then spends thirty minutes explaining what…
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Meta Muse Glimmer 30B: Run a Local AI Agent on 1 GPU (Full Guide)
Meta’s Muse Glimmer 30B is a new open-weight model designed not simply to chat, but to act. The AI industry has spent years chasing larger and larger models. Now, Meta is betting on something different: make the model small enough to actually live on your device, then optimize it specifically for doing things. On August…
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AI Career Opportunities: 5 Paths and What They Involve
Most guides to AI careers list job titles. That is the least useful part, because the titles are the thing changing fastest. A role advertised as “AI Engineer” at one company is a backend job with an API key; at the next it is applied research. The title tells you almost nothing. So this guide…
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Generative AI Explained: 7 Key Insights
There is no shortage of articles explaining what generative AI is. Most of them define the term, list some tools, and leave you no better equipped than before. This one is aimed at the next step: the things that turn out to matter once you actually build something and put it in front of people.…
