[Learning]
Build better AI models. Own your intelligence.
Practical, sourced guides for teams that want more than an API call: when to prompt, retrieve or tune; how post-training works; and how to prove a model is better and cheaper on your own data before it ships.
[Start here]
Featured guides
GuideIntermediate
The complete guide to LLM post-training in 2026
LLM post-training is everything done to a pretrained model to turn raw next-token prediction into useful, reliable behavior: supervised fine-tuning, preference optimization, reinforcement learning and distillation. For most enterprises the question is not which algorithm is best, but whether a measured training run beats a well-built prompt or retrieval system on their own task.
ComparisonBeginner
Open-weight vs proprietary LLMs: what should enterprises choose?
Choose a proprietary API when you need the strongest general model with no infrastructure, and open weights when control, data residency, customization or cost at high volume matter more. Most enterprises end up hybrid: frontier APIs for open-ended work, a tuned open-weight model for narrow, high-volume tasks.
ComparisonBeginner
Fine-tuning vs RAG vs prompt engineering: a practical comparison
Prompt engineering changes the instructions, RAG changes the context the model sees, and fine-tuning changes the model's weights. Start with prompting, add retrieval when answers depend on facts the model lacks or that change, and fine-tune when you need consistent format, style or behavior, or lower cost and latency at scale.
[Learning paths]
Pick a path
01
Decide
For CTOs and AI leaders
Whether to build on open-weight models, and whether your task needs tuning at all.
02
Build
For ML engineers
How post-training works and how to run it on your own data.
03
Measure
For anyone shipping a model
How to prove a change helped before it reaches users.
[Library]
All articles
StrategyPlaybookIntermediate
OpenAI fine-tuning is shutting down: how to migrate your fine-tuned models to open-weight models
OpenAI fine-tuning shutdown dates verified, plus a dated checklist to export data and evals, re-train with LoRA, prove parity and cut over.
Post-trainingGuideIntermediate
The complete guide to LLM post-training in 2026
LLM post-training explained: SFT, DPO, RLHF, GRPO, LoRA, data and evaluation, plus a decision framework for when it beats prompting or RAG.
StrategyComparisonBeginner
Open-weight vs proprietary LLMs: what should enterprises choose?
Open-weight vs proprietary LLMs compared on licences, quality gap, cost at volume, data control and vendor risk, with a decision framework and checklist.
WorkflowsComparisonBeginner
Fine-tuning vs RAG vs prompt engineering: a practical comparison
Fine-tuning vs RAG vs prompt engineering compared: what each changes, costs, when each wins, a decision flowchart, and how to test which works on your data.
Post-trainingComparisonAdvanced
SFT vs DPO vs RL: understanding modern post-training methods
SFT vs DPO vs RL compared precisely: the loss each optimizes, the data it needs, compute and stability trade-offs, failure modes and when to use which.
Post-trainingGuideIntermediate
Synthetic data: how to generate training data for better models
How to generate synthetic data for fine-tuning: generation methods, verification, dedup, decontamination, model collapse, licences, PII, plus working code.
StrategyPlaybookIntermediate
How to build domain-specific models from proprietary data
A playbook for building a domain-specific LLM from proprietary data: task choice, data prep, leak-free splits, RAG vs fine-tuning and governance.
TutorialsTutorialIntermediate
How to fine-tune an open-weight LLM with LoRA and QLoRA
Fine-tune an open-weight LLM with LoRA and QLoRA: current TRL and PEFT code, memory math, 2025 hyperparameter evidence, evaluation and vLLM serving.
EvaluationGuideIntermediate
How to build a custom evaluation framework for LLMs
Build an LLM evaluation framework for your own task: eval set design, sample sizes, metrics, calibrated LLM judges, regression suites and monitoring.
EvaluationGuideAdvanced
How to measure whether fine-tuning actually improved your model
Measure fine-tuning improvement properly: strong baselines, a held-out test set, paired bootstrap and McNemar tests, sample sizes, regressions and cost.
CostPlaybookIntermediate
How to reduce enterprise LLM costs without sacrificing quality
A ranked playbook to reduce LLM costs: measurement, output limits, prompt caching, batch APIs, routing, tuned small models and self-hosting break-even math.
CostExplainerAdvanced
Model distillation: building smaller, cheaper, specialized models
How LLM distillation works: soft targets, sequence-level KD, teacher-generated data and on-policy distillation, plus when it fails and what API terms allow.
WorkflowsGuideIntermediate
How to build custom AI workflows that improve output quality
Design custom AI workflows that beat a single prompt: chaining, routing, verification, tuned models per step, observability, budgets and a data flywheel.
[Hands-on]
Tutorials
Intermediate13 min
How to fine-tune an open-weight LLM with LoRA and QLoRA
Fine-tune an open-weight LLM with LoRA and QLoRA: current TRL and PEFT code, memory math, 2025 hyperparameter evidence, evaluation and vLLM serving.
[Your task, your data]
Want a measured answer for your own use case?
We look at your task and your data, compare against what you run today, and recommend prompting, retrieval or a tuned model only when the numbers support it.