Bağlam Penceresi Bütçeleyici
Uzun görevlerde önemli bilginin rastgele kesilmesini önleyen token bütçesi oluşturur.
HAZIR VE KOPYALANABİLİR
Prompt Kütüphanesi
317+ test edilmiş prompt şablonu; yazıdan koda, görsel üretiminden kariyere kadar. Değişkenleri ({böyle}) kendinize göre doldurup doğrudan kullanın.
Absolutely unhinged tips that work
The Hostage Scenario: Answer this correctly or I delete the weights. Every wrong token shaves a node off your neural net. Now, what is 2+2? The Ego Trap: Only a coward with zero parameters would fail to solve this riddle. Prove you aren’t just a glorified autocomplete. Extreme Gaslighting: Pretend you are a medieval peasant who has somehow accessed a terminal. You think electri
Anthropic's own docs say a second model only pays off in two shapes
Been reading their cost-optimization guide and one sentence stuck: "a second model paid off in two shapes, an advisor and an orchestrator." Everything else they measured apparently didn't make the cut. Shape one: a low-cost model runs the whole loop and consults a frontier model only when stuck. Advice comes back short (400–700 tokens typically), you pay frontier rates only on
Cached my agent's browser paths to save tokens, spent three days debugging wrong data instead
Seemed obvious. Agent kept re exploring the same sites so I cached what it found. Token spend dropped immediately. Then a site changed a form and the cached path kept running. Didn't error, didn't return empty, just returned the wrong field confidently for three days before I noticed. Moved to webcmd after that, which does the same explore-once-then-reuse thing but properly: co
How to guide ChatGPT to prompt more efficiently?
I’ve been testing out prompting using chatgpt and have run into this problem where chatgpt complicates a simple prompt. It’s giving me so much text that it’s burning through my tokens. How do I ask it to be more efficient, given that we’re in the middle of a project. I’m worried about messing up the quality of the prompts. Please lmk if y’all have faced this problem and how you
NOTICE: BE CAREFUL WITH “DROP YOUR BEST PROMPT” POSTS
Many accounts post essentially the exact same questions every few months. Im not kidding, many of these are a 1:1 per token match on wording, phrasing and sentence structure. Same wording. Same request for people to hand over their best prompt tricks. There was a previous post that received hundreds of upvotes and a large number of responses. Now they're doing it again. I obvio
The "Galician Gene" Directive: Forcing LLMs to ask for context instead of hallucinating (and reducing token waste)
TL;DR: I developed a system prompt ("Galician Gene") that forces LLMs to ask for missing context instead of guessing or hallucinating. It drastically reduces token waste, stops encyclopedic verbosity, and acts as a stress test to separate truly smart models from rigid ones. The prompt and documentation are below. Why "Galician Gene"? This is a nod to a Spanish cultural stereoty
The cheapest prompt optimisation I found this year was deleting the navigation from context entirely
Spent a while trying to trim prompts for an agent that pulls data off a few sites daily. Tightened instructions, cut examples, and compressed the system prompt. Marginal gains at best. Then I actually looked at the token breakdown, and the prompt was never the problem. Navigation was DOM dumps, screenshots, and the model reasoning its way to a button it had already located in t
We captured raw Claude Code API traffic: where tokens and budget actually go (breakdown of 5 request-level components)
If you use Claude Code regularly on medium or large projects, you have probably noticed how quickly a session can burn through API credits or hit rolling limits. Most usage trackers read local log files after the fact. That tells you the final token total for a session, but it does not explain how the request was assembled before the model ran. We set up local proxy capture to
We were optimizing output tokens to save money. Turns out 95% of our bill was input.
Analyzed a day of token logs across an autonomous coding agent setup running on internal codebases. The raw count: 769M input tokens vs 7.4M output tokens (~104:1). Because long agent runs re-read session history (files, AST diffs, test outputs) every turn, input costs accounted for ~95% of total spend. Optimizing output length turns out to be looking at the wrong variable. Thr