AI Text Watermarking in Python, Three Families Tested
Hands-on AI text watermarking in Python: how the three families work, what survives copy-paste, edits, and paraphrasing, and when to watermark or detect.
Category
Guides
Hands-on tutorials and explainers for working with AI: prompting, tools, workflows, APIs, and the concepts behind them, with working examples and honest trade-offs.
14 posts
Prompt Dependency Graphs That Shrink Your Retest Set
Build a prompt dependency graph to compute the blast radius of any prompt change and rerun only the evals your multi-prompt LLM system needs.
NLP vs LLM vs RAG, Routed by Task Shape and Cost
NLP vs LLM vs RAG is a routing decision set by task shape. Compare the cost math, failure modes, and an LLM-fallback pattern before picking a model.
Build an AI Text Detector, Then Test If It Can Ship
Build an AI text detector with small local models, stress-test it on short and human-edited text, and use the error rates to decide if it ships.
LLM Context Window Management With a Token Budget
Learn LLM context window management with a token budget ledger, a stepwise compression ladder, and the prompt cache trap that punishes trimming.
Structured Output Local LLM Tactics That Survive Production
Structured output local LLM enforcement means choosing JSON mode, grammar decoding, or tool-calling. Each trades latency, throughput, and reliability.
Control Reasoning Effort LLM APIs in Production
Control reasoning effort LLM APIs across OpenAI, DeepSeek, and Anthropic. Practical routing rules to cut cost and latency without losing accuracy.
Shipping LLM Browser Agents Without Breaking Production
LLM browser agents look easy in demos. Shipping one means defaulting to APIs, containing prompt injection, and budgeting for unpredictable per-step costs.
Run AI Models Locally on Mac With MLX and Nativ
Run AI models locally on Mac with MLX and Nativ. A trade-off framework for when on-device inference beats APIs on cost, privacy, and latency.
Fine-Tuning vs RAG vs Prompt Engineering Decision Framework
Fine-tuning vs RAG vs prompt engineering: run this eval-gated framework before training a custom model and inheriting its hidden maintenance tax.
Building Proactive AI Agents With Context Graphs
Proactive AI agents use context graphs to cut time-to-surface from 47 minutes to under 30 seconds, eliminating reactive RAG blind spots in enterprises.
Build an LLM Fallback Strategy Before Access Drops
Prepare for AI model deprecations and government access restrictions. Build a resilient LLM fallback strategy using multi-provider routing and open-weights.