YOUR OLD GROK BOT SETUP IS THE PROBLEM SpaceXAI just admitted the old rules you gave Grok Bot are making it pricier and dumber. 75% of your tokens burned on pointless work. a $200 plan delivering $50 worth of actual results 20 GitHub repos to fix the setup that’s... wasting your tokens and dragging down your results: 01 Promptfoo → compare your current instructions against a cleaner version on the same test cases, so removing a rule becomes something you can evaluate 02 DSPy → optimize instructions and examples against a defined quality metric, replacing endless manual prompt edits with measured experiments 03 SAMMO → experiment with prompt structure, wording, and compression while checking whether the task still gets done correctly 04 PromptWizard → generate, critique, and refine candidate instructions using task examples, then test the revised prompt before adopting it 05 DeepEval → turn recurring failures into repeatable evaluations so a shorter prompt doesn’t quietly lose requirements your workflow depends on 06 Inspect AI → evaluate complete interactions, including tool use and multi-turn behavior, to catch instructions that send the workflow down unnecessary paths 07 Langfuse → trace instrumented runs and track prompt versions, helping you connect a configuration change to repeated calls or declining output quality 08 OpenLIT → monitor usage, cost, and latency in connected AI workflows so you can identify expensive steps before rewriting the whole setup 09 OpenLLMetry → add tracing to supported model libraries and frameworks, making the sequence of calls behind an expensive result easier to inspect 10 Helicone → log and analyze API requests to find costly patterns, failed calls, and changes in spending across your instrumented workflow 11 Pydantic AI → enforce request, token, and tool-call limits in Python workflows so a written instruction isn’t your only control over repeated actions 12 LangGraph → preserve workflow state through checkpoints, allowing a failed step to resume without rebuilding everything that already succeeded 13 Tenacity → put retry counts and stopping conditions in your Python tools instead of relying on another paragraph telling the bot when to give up 14 jsonschema → check required fields, types, and output structure in code, reducing the need for repeated model calls to inspect predictable formatting 15 JMESPath → extract the fields your task needs from JSON responses before the model reads them, keeping irrelevant API data out of the conversation 16 yq → inspect and transform YAML, JSON, and other configuration files so you can isolate relevant settings during an audit 17 DiskCache → store reusable script results on disk so recurring jobs can reuse unchanged work across runs 18 LLMLingua → compress lengthy reference material before it enters the model’s context, then check that the details your task needs survived 19 Git → version your prompts and configuration files so you can identify when a harmful rule appeared and restore a working setup 20 delta → inspect readable diffs of instruction and configuration changes, making small additions easier to spot before comparing their effect on results rules like “verify twice” and “be maximally thorough” can turn a simple task into unnecessary checks. conflicting instructions, stale examples, and rigid procedures add more work before the bot even reaches the answer audit the instructions, skills, routines, and tool settings your workflow actually uses. locate each questionable rule, explain its likely effect, and propose a specific change test one edit on the same recurring job. preserve real requirements, quality checks, and approval boundaries. keep the revision when it reduces usage without making the result worse save this. the most expensive line in your setup might be the one you forgot you added the full guide below shows how to shrink working context, contain retries, and turn the saved budget into more completed work ↓show more

Gresham

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