LLM – Vectoring AI

Prompt Engineering vs Context Engineering

From crafting single prompts to designing dynamic systems that give LLMs everything they need to succeed

Observability for Multi-Turn LLM Conversations with LangSmith

End-to-end guide: trace, monitor, and debug multi-turn agentic conversations with LangChain, LangGraph, and LangSmith — covering threads, runs, tool use, token cost, latency, and error tracking

Deploying and Serving LLM with Llama.cpp

End-to-end guide: deploy and serve LLMs locally and at scale with llama.cpp for efficient CPU/GPU inference

Deploying and Serving LLM with vLLM

End-to-end guide: deploy and serve LLMs at scale with vLLM for high-throughput, low-latency inference

Fine-tuning an LLM with Unsloth and Serving with Ollama

End-to-end guide: fine-tune a small model on Hugging Face with Unsloth and deploy locally with Ollama

Run LLM Locally with Ollama

From setup to deployment: run and serve local LLMs easily with Ollama

Training LLMs for Reasoning

A practical guide to building reasoning capabilities in language models using RL, distillation, and chain-of-thought training

FinOps Best Practices for LLM Applications

From prompt caching to model routing: a practical guide to cutting LLM inference costs by 10x with semantic caching, continuous batching, quantization, prompt optimization, and cost-aware architecture

Post-Training LLMs for Human Alignment

A practical comparison of SFT, RLHF, DPO, ORPO, KTO, and GRPO for aligning pretrained language models with human preferences

Guardrails for LLM Applications with Giskard

Securing LLM applications in production: policy-based content screening, jailbreak detection, PII filtering, groundedness checks, and custom guardrails using Giskard Guards and Giskard Open Source

Quantization Methods for LLMs

A practical comparison of GPTQ, AWQ, GGUF, and bitsandbytes for compressing pretrained language models

Scaling LLM Serving for Enterprise Production

From a single GPU to millions of requests: hardware foundations, serving engines, parallelism strategies, load balancing, Kubernetes orchestration, and production monitoring for on-premise LLM deployment

Decoding Methods for Text Generation with LLMs

A hands-on comparison of greedy search, beam search, sampling, top-k, top-p, and contrastive search using small pretrained models

Training LLMs with Mixture of Experts

From dense to sparse: understanding MoE architecture, routing strategies, expert specialization, sparse upcycling, and fine-tuning MoE models with PyTorch and Unsloth

Pre-training LLMs from Scratch

End-to-end guide: from web scraping and data collection to preprocessing, tokenization, and pretraining a small language model with PyTorch and Unsloth