Improving Determinism with LLMs: Prompting, Model Selection, Context, and Tools

<p>You opened ChatGPT before Slack this morning.<br /><br /> Not for anything complicated. Just to figure out how to begin.</p> <p>That small move is already changing what

<p>57% of organizations estimate their data isn't AI-ready. General-purpose LLMs handle broad tasks well but hallucinate on specialized queries, miss domain jargon, and can't

<p>Claude's context window holds 200,000 tokens. Gemini's stretches to two million. But response quality starts degrading long before the window fills up. Window size doesn't

<p>Here's something you'll notice after running AI agents in production for a few weeks: a fresh conversation with your agent is sharp. Give that same agent 40 messages of history

<p>At the International AI Summit in Dublin, OpenAI Chief Financial Officer Sarah Friar took the stage to announce that the lab is holding back a breakthrough model. According to

<p>The easiest way to make an LLM application look smart is to give the model more context.</p> <p>The harder problem is deciding what context deserves to be there.<br /> While

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<h1> Static Analysis for LLM Prompt Security: A Methodology for Pre-Deploy Vulnerability Detection </h1> <p><em>How applying SAST principles to LLM prompt strings catches security