AI & Prompt Engineering Hub
The essential toolkit for AI engineers and prompt designers. Calculate tokens for GPT-4 and Claude, analyze prompt costs, and generate system prompts with precision.
Thinking in tokens, not words
Building with LLMs in 2026 is a different discipline than calling a REST API. The cost model is token-based, the quality depends on prompt architecture, and the "right" model depends on your specific task, not benchmarks someone else ran. This hub gives you the measurement tools to make those decisions with data instead of vibes.
Token counting is the foundation. Tokens are not words, GPT-4's cl100k_base tokenizer encodes "developer" as 1 token but "devops" as 2. The token counters here use the actual tokenizer libraries (tiktoken for OpenAI, anthropic-tokenizer for Claude) so counts match billing to the token. Cost estimation then multiplies token counts by per-model pricing, input tokens are typically 3-10× cheaper than output tokens, which biases prompt design toward "long input, short output."
The system prompt builder addresses the highest-impact optimization in LLM app development. A well-structured system prompt cuts token usage 30% and improves consistency more than any amount of few-shot examples. The builder provides templates for common patterns: role-setting, output formatting, constraint enforcement.
Model comparison is a moving target, capabilities and pricing change monthly. The comparison tool here shows side-by-side specs for GPT-4o, Claude Sonnet/Opus, Gemini, and open-source models. The key decision axes: context window size, multimodal support, output quality for your task type, and cost per million tokens.
Featured Tools
All AI & Prompt Engineering Hub Tools
Quick Summary
Building with LLMs in 2026 means thinking in tokens, costs, and prompt architectures, not just 'send a string to OpenAI.' This hub gives you tokenizers for every major model, a cost estimator, prompt builders, and side-by-side model comparisons so design decisions are data-driven instead of vibes-driven.
Key Takeaways
- Tokens ≠ words, `cl100k_base` (GPT-4) tokenizes 'developer' as 1 token but 'devops' as 2; count accurately before estimating cost.
- Different models have different tokenizers, Claude, GPT-4, Gemini, Llama all count differently for the same input.
- Input tokens are usually 3–10× cheaper than output tokens, bias prompt design toward 'long input, short output.'
- Prompt caching (Anthropic, OpenAI) can cut input cost by 80–90% for repeated system prompts, measure cache hit rate.
- System prompts multiply your effort: a well-structured one cuts token usage 30% and improves consistency dramatically.
When to use it
- Estimating monthly cost of a feature before committing to a model (GPT-4o vs Claude Sonnet vs Llama 3).
- Counting tokens for a long-context use case to verify it fits in the model's window.
- Iterating on system prompts with structured templates instead of one-line strings.
- Comparing capabilities and pricing across providers when choosing an LLM backbone.
Common Mistakes
- Estimating costs based on word count, undercounts tokens by ~30% for English, more for code/JSON.
- Sending the full conversation history every call without caching, burns money on identical prefixes.
- Hardcoding model names like `gpt-4` instead of using model aliases, breaks when the provider deprecates.
- Ignoring streaming for user-facing UX, non-streaming responses feel ~3× slower to users even at identical latency.
AI & Prompt Engineering Hub, Frequently Asked
How accurate are token counters?
Very, they use the actual tokenizer libraries (tiktoken for OpenAI, anthropic-tokenizer for Claude). Counts match the provider's billing to the token.
Why is output more expensive than input?
Output generation requires running the full model autoregressively token-by-token (compute-intensive). Input is processed once in parallel. Providers price accordingly, usually 3–5× more for output.
Should I use Claude, GPT-4, or open-source?
Depends on task. Claude leads at long context, careful reasoning, code review. GPT-4o leads at multimodal and ecosystem. Open-source (Llama 3, Qwen) wins on cost-sensitive batch jobs where latency tolerates self-hosting.
Tool Comparisons
In-Depth Tutorials
- Prompt Caching Patterns: Reducing Latency and Costs in AI AppsDon't send the same system prompt 1,000 times. Learn how to use Prompt Caching (Claude & GPT-4o) to speed up your AI features and cut your API bill by up to 90%.
- Securing the AI Supply Chain: Why You Need an Internal Audit Hub
- The Modern AI Toolkit: Beyond the Chatbox
- Token Counting & Cost Calculation: Managing LLM Expenses in 2026Stop overpaying for your AI. Learn how tokens are calculated across different models (Claude, GPT-4o, Gemini), how to estimate costs, and strategies for optimizing your context window.