A thread you can test
Cost Optimization & Model Selection
12 notes move from the word to a real choice at work — understand it first, then decide whether to use it.
Each note stands alone, or becomes the next step in this thread.
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is Cost Optimization & Model Selection, and which AI decisions does it change?
Token cost accumulation visualization; drag the turn count to see exponential cost growth This page keeps the related concepts, common mistakes, and practical notes in one reading thread.
First decide whether you are blocked by a definition, a choice, or verification; then choose the closest of the 12 notes below.
Start with “Why Multi-Turn Conversations Get More Expensive,” then restate the conclusion using your own task.
Do not treat every method in a topic as interchangeable. The answer changes with the input, risk, and acceptance bar.
THIS QUESTION THREAD
Put the word back inside the choice it changes.
Why Multi-Turn Conversations Get More Expensive
Token cost accumulation visualization; drag the turn count to see exponential cost growth
KV Cache: Trading Space for Time (and Money)
Analogy-based explanation + savings calculator; drag the turn count to see savings ratio
Explicit Caching: A Practical Comparison
cache_control syntax, cache-hit detection, pricing discounts — real savings demonstration
Dynamic Timestamps: The Most Expensive System Prompt
Bad design vs good design; three timestamp handling approaches side by side
Comprehensive Cost Optimization: A System-Level Approach
5-layer optimization strategy, cost breakdown visualization, system designs that save 70-90%
Image Tokens: Pixels Cost Money Too
Image billing formula, scaling mechanics, resolution traps, task-based tier strategy
Match Resolution to Task
High / medium / low resolution tiers; Token consumption comparison and selection advice by scenario
Syntax-Layer Optimization: Prompts Written for Machines
YAML vs JSON, CSV vs arrays, compressed JSON output — save 10-30% on formatting Tokens
Semantic-Layer Optimization: Don't Treat Context as a Dumpster
Dynamic Few-Shot, LLMLingua-2 compression, key info at start/end — boost information density
Output Layer + Advanced KV Cache
Negative constraints, diff-based polishing, stop sequences; KV Cache tool traps and sliding-window issues
Model Selection: Capability vs Cost
Mainstream model capability/cost matrix, selection decision tree, model-matching strategies by scenario
Simplicity First: Stick to First Principles
The essence of AI Harness / build vs skip trade-offs / what will be obsoleted / the ultimate question