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PHP2Shell: The PHP Bug
Akshay Thakare

LLMs

Model internals, fine-tuning, quantisation and inference.

4 notes · all topicsGenAI & LLMs

Next · intermediate3

  1. 1LLM Inference: Cost and Latency in ProductionWhere time and money go in an LLM call, and the techniques that cut both: smaller models, caching, streaming, batching and shorter prompts.
  2. 2RAG vs Fine-Tuning vs Prompting: Which One?A decision guide: when better prompts are enough, when to add retrieval, and when fine-tuning is actually worth the cost.
  3. 3Evaluating LLM Applications Before and After LaunchBuild an evaluation set, choose checks that match the task, use model-based grading carefully, and keep measuring once real users arrive.

Go deeper · advanced1

  1. 4LoRA and QLoRA: Fine-Tuning Without a GPU FarmTrain a few million adapter parameters instead of billions, and when fine-tuning beats prompting or RAG.