research guides - 2026
Deep AI engineering guides for builders who need more than tool lists.
Practical, source-backed pages on model training, RAG, voice cloning, speech recognition, video LoRAs, music generation, and agent evals.
How to Train a Text LLM With LoRA, QLoRA, SFT, DPO, and GRPO
A practical 2026 playbook for adapting open text models with supervised fine-tuning, preference optimization, and reinforcement-style post-training.
+ Start with SFT only when you need the model to change behavior, format, or domain language. Use RAG for changing facts.
+ LoRA and QLoRA are the default first experiment because they keep GPU cost down and preserve the base model.
+ Preference training matters after SFT when the issue is ranking, refusal style, verbosity, or tool-use behavior.
+ Use an eval set before training. Otherwise every checkpoint will sound better during manual testing.
all guides
Model training and AI systems
15 guides
RAG vs Fine-Tuning for Private Data: The Production Decision Guide
How to decide whether to retrieve private knowledge, fine-tune behavior, or combine both for enterprise AI systems.
Training a Separate Voice Model: TTS Fine-Tuning, Data, Consent, and Evaluation
A grounded guide to voice-cloning and text-to-speech model training with XTTS, StyleTTS 2, speaker data, transcripts, and safety checks.
Fine-Tuning Whisper and ASR Models for Domain Audio
How to build a labeled speech-recognition dataset, fine-tune Whisper-style ASR, and evaluate word error rate by domain slice.
Training Video LoRAs: Wan, CogVideoX, LTX, Data Manifests, and Motion Evals
A 2026 guide to adapting open video generation models with small video datasets, LoRA, captioning, and motion-specific evaluation.
Training Music Generation Models With AudioCraft, MusicGen, and Stable Audio
How to prepare licensed music datasets, fine-tune text-to-music models, and evaluate style, structure, BPM, and legal risk.
AI Agent Evals in Production: Tool Use, RAG, MCP, and Regression Testing
A practical framework for evaluating AI agents before model upgrades, prompt changes, tool additions, or MCP server rollouts.
Muse, Diffusion, DreamBooth, and LoRA: Training Image Generation Systems in 2026
A research-grounded guide to image model adaptation, from Google Muse-style masked image transformers to practical Diffusers LoRA and DreamBooth workflows.
Agentic AI Frameworks Compared: LangGraph, AutoGen, CrewAI, OpenAI Agents, MCP, and A2A
A practical decision guide for choosing agent frameworks, protocols, memory, tools, handoffs, and human review patterns in 2026.
Multimodal AI Systems: How to Combine Vision, Audio, Video, Text, Search, and Tools
A practical architecture guide for systems that mix text models, vision encoders, speech models, video understanding, retrieval, and tool-using agents.
Confidential AI: GPU TEEs, Attestation, Private Inference, and Secure Fine-Tuning
A practical guide to confidential AI infrastructure: GPU trusted execution environments, remote attestation, encrypted data flows, and privacy boundaries.
On-Device and Browser AI: WebGPU, WebNN, Gemini Nano, Apple Foundation Models, and Edge Runtimes
How to design AI features that run locally in the browser or on device, with privacy, latency, model-size, and fallback tradeoffs.
LLM Inference Optimization: vLLM, TensorRT-LLM, KV Cache, Quantization, and Speculative Decoding
A deployment guide to making LLM inference faster and cheaper without destroying quality, covering serving engines, batching, cache design, quantization, and decoding.
Synthetic Data and Model Distillation: How to Generate Training Data Without Poisoning Your Evals
A practical framework for synthetic data generation, teacher-student distillation, data filtering, eval hygiene, and model improvement loops.
Computer-Use Agents: Browser Automation, Desktop Control, Security, and Evaluation
How to design agents that operate websites or desktops safely, with action spaces, screenshots, sandboxes, human review, and benchmark-aware evals.