Tagged llm

Categorize LLM pipeline failure modes by reading traces after changes or incidents

Audit an LLM eval pipeline for missing error analysis and unvalidated judges

Create diverse synthetic test inputs for evaluating an LLM pipeline

Calibrate an LLM judge against human labels using TPR, TNR and bias correction

Design LLM judge prompts for subjective checks like tone, faithfulness and completeness

Find and fix LLM application risks using the OWASP Top 10 for LLMs

Benchmark a third-party LLM endpoint for availability, speed, fidelity and quality

Find and download AI model and provider brand logos in SVG, PNG and WEBP

Build a personal investment research wiki in markdown with wikilinks

Configure Promptfoo evals with LLM-as-judge rubrics and custom Python assertions

Run LLM wiki workflows in Codex for ingest, compile, query, lint and outputs

Build and manage LLM-compiled wikis: ingest sources, compile, query and audit

Query an LLM wiki read-only through its index with exact citations

Create LLM-first agent skills with valid frontmatter and clear triggers

Audit any domain for AI crawler access, llms.txt, schema and SSR, scored 0 to 100

Analyze LLM spend by call site and model, then cut costs with smarter profiles

Connect an LLM provider, discover model IDs and verify a model profile from the CLI

Build evals that measure AI product quality with human, code and LLM judges

Design, evaluate and tune LLM prompts and system instructions

Run LLM evaluations on 60+ academic benchmarks such as MMLU, HumanEval and GSM8K

Fine-tune large LLMs efficiently with LoRA, QLoRA and other adapter methods

Speed up LLM fine-tuning with Unsloth and cut GPU memory use with LoRA

Test LLM apps for prompt injection, system prompt leaks and unsafe tool use

Swap LLM routing calls for decision models in LangGraph and audit existing graphs

Explain machine learning, LLMs and speech with an intuition-first teaching style

Review generated code for Clean Code, SOLID and LLM failure modes before it ships

Run LLMs and vision models on Apple Silicon with MLX Swift, including LoRA tuning

Remove refusal behavior from open-weight LLMs using interpretability methods

Extract and normalize pricing tiers from SaaS, API and LLM vendor pricing pages

Harden LLM apps against prompt injection, tool-call abuse and unsafe outputs

Harden LLM deployments against injection, exfiltration and model theft

Run incident response for LLM outages, quality drops and runaway AI costs

Secure LLM apps with input checks, output controls and tenant isolation

Cut LLM API costs and latency with exact, semantic and provider prompt caching

Lower LLM spend with model routing, batching, budgets and cost tracking

Set up QLoRA and full fine-tuning infrastructure with TRL, Axolotl and DeepSpeed

Front LLM APIs with a gateway for rate limits, key management, fallbacks and costs

Autoscale LLM inference on Kubernetes with KEDA, GPU metrics and spot nodes

Turn a Mac mini into an always-on private local LLM server with remote access

Design multi-tenant LLM hosting with isolation, quotas and billing attribution

Run local LLMs with Ollama and Open WebUI for private dev environments

Generate structured JSON, regex-constrained or Pydantic-typed output from LLMs

Tune a skill file against an LLM judge rubric until a test agent performs well

Build LLM-powered features with prompt engineering, RAG and chatbot patterns

Replay past Agentforce conversations against a different LLM to compare

Inspect LLM profiler traces for layer timings and kernel boundaries

Benchmark SGLang, vLLM and TensorRT-LLM to find the best serving command for a model

Parse SGLang and vLLM startup logs to estimate KV cache and max concurrency

Find kernel hotspots, fusion gaps and overlap in LLM serving profiler traces