LLM Engineering
Build production apps with LLM APIs — function calling, structured output, streaming.
0%
Confidence · no data yet
After this skill you can…
- Call LLM APIs with function/tool use
- Parse structured JSON from LLM output
- Handle streaming responses
- Implement retry/fallback logic
Prerequisites
Watch (10 videos)
LLM Quantization Explained
→ Implement quantization using Llama.cpp→ Optimize model performance using GGUF file format
Become an AI Engineer in 2026 | Microsoft AI Engineer Program | #Shorts| #Simplilearn
→ Design AI solutions→ Deploy AI models→ Build AI-powered products
What Is Claude Opus 4.8? | Opus 4.8 Explained | #Shorts #Simplilearn
→ Develop dynamic workflows with parallel sub-agents→ Optimize effort control in AI models→ Integrate Claude Opus 4.8 into existing systems
Hidden Codex Setting
→ Configure LLM settings→ Optimize resource usage→ Utilize maximum reasoning effort
What is Memory Engineering? Agentic AI Engineering Explained (2026)
→ Design memory engineering for AI solutions→ Implement short-term and long-term memory in LLMs→ Apply semantic, episodic, and procedural memory in AI engineering
NEW Claude Code Update Changes EVERYTHING!
→ Optimize AI search speed→ Implement precise permissions→ Control sub-agents
Heretic 12B — Uncensored Gemma 4 Running Locally on Ollama (GPU Crash Included)
→ Fine-tune LLMs→ Deploy LLMs on local machines→ Build custom model manifests
What's one mistake devs make when building cost-efficient AI apps?
→ Design end-to-end AI systems→ Optimize AI models→ Integrate AI with data sources
Claude Code: Building Agent Operating Systems!
→ Build Agent Operating Systems→ Create AI Agents→ Automate Tasks
Kimi K2.7 Code is Here, Beats Claude Opus 4.8 on Real-World Coding Benchmarks
→ Build coding-focused agentic models→ Optimize model parameters for efficiency→ Deploy models on Hugging Face and Moonshot AI's platform
DeepCamp AI