How LLMs 'think' and probabilistic outputs

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Here are the top 5 takeaways from this episode:

* Competency requires using AI tools correctly. Model Rule 1.1 obligates lawyers to be competent in technology they use. Using the wrong tool (e.g., ChatGPT for legal research) isn’t just ineffective — it’s potentially unethical. Use purpose-built, citation-backed tools like Paxton AI or Perplexity Pro instead.

* Billable hours are becoming ethically questionable. Model Rule 1.5 Comment 5 prohibits billing by the hour using “wasteful procedures.” If AI can do 10 hours of work in 10 minutes, continuing to bill by the hour may violate your ethical obligations. The billable hour model is fundamentally at odds with AI-driven efficiency.

* Retrieval-Augmented Generation (RAG) is the gold standard. The most reliable AI outputs come from tools that retrieve from a curated database before generating a response. Tools like NotebookLM Pro (upload your own sources) or Paxton AI (legal case law database) minimize hallucinations and provide citable, verifiable answers.

* Context is everything. Telling an AI who you are, who you represent, and what you need dramatically improves output quality. Treating AI like a brilliant but new employee — giving it context, iterating, and reviewing its work — is the right mental model for effective use.

* The subscription model is the path forward. With 77% of people unable to afford hourly legal fees, there’s a trillion-dollar latent legal market. Subscription-based pricing offers predictable revenue, scales without more hiring, reduces burnout, and lets lawyers serve clients who’ve been priced out of the billable hour system entirely.

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