--- author: "Umesh Malik" canonical: "https://umesh-malik.com/blog/tag/llm-engineering" description: "Explore articles tagged with LLM Engineering by Umesh Malik — AI Engineer, LLM & GenAI Developer. Learn LLM Engineering best practices, practical tips, and in-depth guides." title: "Umesh Malik's Blog - LLM Engineering Articles | LLM Engineering Tutorials" tokens: 2304 generator: "scripts/generate-page-markdown.mjs" --- [← Back to Blog](https://umesh-malik.com/blog) # LLM Engineering 17 articles [![Cover showing the inference engine attack surface with token stream flowing from model through vulnerable parser to arbitrary code execution, and the defense architecture separating GPU host from token parsing](https://umesh-malik.com/blog/secure-llm-inference-vllm-cve-2025-9141-cover.png) AI Security • Aug 25, 2026 ### How to Harden vLLM Inference: CVE-2025-9141 Defense Guide How to harden vLLM inference against token exploits. CVE-2025-9141 let models run code via eval(). Separate GPU hosts from parsers. 9 min read Read more →](https://umesh-malik.com/blog/secure-llm-inference-vllm-cve-2025-9141) [![Chart showing ChatGPT Search site-scoped query share jumping from 0.3% to 17% on August 8, 2026](https://umesh-malik.com/blog/chatgpt-search-site-scoping-geo-cover.png) LLM Engineering • Aug 24, 2026 ### ChatGPT Search Optimization After the Site-Scoping Shift ChatGPT search optimization changed when 17% of queries started scoping to specific sites. What the GPT-5.6 shift means and how to get cited. 7 min read Read more →](https://umesh-malik.com/blog/chatgpt-search-site-scoping-geo) [![How server-side compaction replaces a long agent transcript with a single summary block once input tokens cross the trigger, and what is kept versus permanently dropped](https://umesh-malik.com/blog/agent-context-compaction-what-survives-cover.png) AI Engineering • Aug 12, 2026 ### Agent context compaction: keep what the 150K cutoff drops Agent context compaction drops every block before the summary at 150K tokens. What survives, what instructions silently replaces, and the usage field that lies. 9 min read Read more →](https://umesh-malik.com/blog/agent-context-compaction-what-survives) [![Timeline comparing the knowledge-cutoff date vendors claim for Claude Opus 5 against the earlier cutoff its answers actually reveal](https://umesh-malik.com/blog/testing-llm-knowledge-cutoffs-opus-5-cover.png) LLM Engineering • Aug 11, 2026 ### How to Test an LLM's Knowledge Cutoff: Opus 5's May Claim Falls Short Here's how to test an LLM's knowledge cutoff with three reproducible probes — the method showing Opus 5 claims May 2026 but answers like January 2026. 7 min read Read more →](https://umesh-malik.com/blog/testing-llm-knowledge-cutoffs-opus-5) [![Cover showing the vLLM VRAM budget split into model weights, runtime overhead and KV cache, with the KV cache block math that converts free VRAM into concurrent sequences](https://umesh-malik.com/blog/vllm-throughput-tuning-flags-cover.png) LLM Engineering • Aug 8, 2026 ### vLLM throughput tuning: configure these four flags, not a bigger GPU vLLM throughput tuning starts with KV cache blocks, not a bigger GPU. The four flags that decide your tokens/sec, and the one that quietly backfires. 10 min read Read more →](https://umesh-malik.com/blog/vllm-throughput-tuning-flags) [![Diagram contrasting a sequential agent loop of ten model round trips with a two-level dependency graph running nine tool calls concurrently](https://umesh-malik.com/blog/parallel-agent-tool-calls-dag-harness-cover.png) AI Engineering • Aug 6, 2026 ### Run agent tool calls in parallel: 10 turns become 2 DAG levels Run agent tool calls in parallel by swapping the loop for a DAG planner: ten round trips become two levels, plus the cap, budget and critic on top. 10 min read Read more →](https://umesh-malik.com/blog/parallel-agent-tool-calls-dag-harness) [![Reinforcement fine-tuning: a 4B open model matching a frontier LLM on retrieval at a fraction of the cost](https://umesh-malik.com/blog/reinforcement-fine-tuning-small-models-retrieval-cover.png) LLM Engineering • Aug 6, 2026 ### Reinforcement Fine-Tuning: When a 4B Model Beats GPT-5.6 Reinforcement fine-tuning let a 4B open model match GPT-5.6 Sol on retrieval at 100x lower cost. How RFT works, and when it beats prompting a frontier LLM. 10 min read Read more →](https://umesh-malik.com/blog/reinforcement-fine-tuning-small-models-retrieval) [![Agent harness design diagram: a model's reasoning discarded each turn versus reasoning retained and context compacted across turns](https://umesh-malik.com/blog/agent-harness-design-arc-agi-3-cover.png) AI Engineering • Aug 5, 2026 ### Agent Harness Design: Why an ARC-AGI-3 Score Tripled Agent harness design decided a benchmark: OpenAI's ARC-AGI-3 score went 13.3% → 38.3% with zero model changes. What that means for your agent loop. 11 min read Read more →](https://umesh-malik.com/blog/agent-harness-design-arc-agi-3) [![LLM abuse detection diagram contrasting per-message content classification with account-level behavioural correlation across a session corpus](https://umesh-malik.com/blog/llm-abuse-detection-openai-scam-network-cover.png) AI Security • Aug 5, 2026 ### LLM Abuse Detection: What OpenAI's Scam Ban Reveals LLM abuse detection failed at the message level and worked at the account level. OpenAI's Cambodia scam ban shows which signal actually catches misuse. 11 min read Read more →](https://umesh-malik.com/blog/llm-abuse-detection-openai-scam-network) [![Diagram of an LLM eval framework pipeline: tasks and configs produce runs, graders apply checks to produce grades](https://umesh-malik.com/blog/llm-eval-framework-smevals-cover.png) LLM Engineering • Aug 4, 2026 ### LLM Eval Framework: Grade Prompts, Models and Harnesses An LLM eval framework turns vibes into scores. How smevals structures tasks, configs, runners and graders — and how to ship your first eval today. 10 min read Read more →](https://umesh-malik.com/blog/llm-eval-framework-smevals) [![Layer-by-layer LLM inference streaming one transformer layer at a time from disk onto a 4GB GPU](https://umesh-malik.com/blog/run-70b-llm-on-4gb-gpu-airllm-cover.png) LLM Engineering • Aug 4, 2026 ### Run 70B LLM on 4GB GPU: AirLLM's Real Tradeoff Run 70B LLM on 4GB GPU hardware with AirLLM's layer-by-layer inference. The VRAM math is real — you just pay for it in disk bandwidth. The honest tradeoff. 11 min read Read more →](https://umesh-malik.com/blog/run-70b-llm-on-4gb-gpu-airllm) [![A RAG chatbot in Next.js: embed the query, search pgvector, augment the prompt, stream a cited answer](https://umesh-malik.com/blog/rag-chatbot-nextjs-guide-cover.png) AI Engineering • Jul 21, 2026 ### Build a RAG Chatbot in Next.js: Retrieval, Streaming & Citations (2026) Build a RAG chatbot in Next.js with the AI SDK: embed the query, search pgvector, stream a grounded answer with citations, and stop hallucinations. 8 min read Read more →](https://umesh-malik.com/blog/rag-chatbot-nextjs-guide) [![The production layer of a Vercel AI SDK app: streaming, tool-calling, abort, rate limiting, and cost control](https://umesh-malik.com/blog/vercel-ai-sdk-production-guide-cover.png) AI Engineering • Jul 21, 2026 ### Vercel AI SDK in Production: Streaming, Tool-Calling & the Gotchas Nobody Tells You (2026) Vercel AI SDK in production: streaming, tool-calling, aborting generations, error retry UX, rate limiting, and cost control — the layer every tutorial skips. 9 min read Read more →](https://umesh-malik.com/blog/vercel-ai-sdk-production-guide) [![Editorial cover: the demo-to-production gap for autonomous AI agents in 2026](https://umesh-malik.com/blog/autonomous-ai-agents-production-gap-2026-cover.png) AI Engineering • Jun 14, 2026 ### Why 77% of Autonomous AI Agents Never Reach Production (2026) Only 23% of autonomous AI agents reach production in 2026. The demo-to-production gap, why agents fail, and the playbook the winners actually use. 9 min read Read more →](https://umesh-malik.com/blog/autonomous-ai-agents-production-gap-2026) [![The stages of a production retrieval-augmented generation pipeline](https://umesh-malik.com/blog/build-rag-pipeline-from-scratch-cover.png) AI Engineering • Jun 8, 2026 ### Build a RAG Pipeline From Scratch: Production Patterns That Matter Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns that decide whether it works. 7 min read Read more →](https://umesh-malik.com/blog/build-rag-pipeline-from-scratch) [![Architecture of a production Model Context Protocol server on Cloudflare Workers](https://umesh-malik.com/blog/how-to-build-mcp-server-cover.png) AI Engineering • Jun 8, 2026 ### How to Build an MCP Server: A Step-by-Step Guide (2026) How to build an MCP server, step by step: JSON-RPC 2.0, the Streamable HTTP transport, typed tools, and agent discovery — from a real one I shipped. 9 min read Read more →](https://umesh-malik.com/blog/how-to-build-mcp-server) [![RAG vs fine-tuning architecture comparison for LLMs](https://umesh-malik.com/blog/rag-vs-fine-tuning-llms-2026-cover.png) AI Engineering • Feb 28, 2026 ### RAG vs Fine-Tuning for LLMs in 2026: A Production Decision Framework With Real Tradeoffs RAG vs fine-tuning for LLMs in 2026: a practical decision framework covering architecture tradeoffs, cost, latency, and when to use each in production. 6 min read Read more →](https://umesh-malik.com/blog/rag-vs-fine-tuning-llms-2026)