For everyone taking AI courses on Maven. Do you know what to do next for your/company's AI systems?.

RT @jxnlco: Document automation isn't just about replacing humans with AI. The real skill is knowing when to fully automate and when to k….

RT @jxnlco: @pashmerepat think RAG is the wrong choice for most coding agents. looking forward to hearing his case today .

RT @jxnlco:

*Part of the "Effective AI Engineering" series - practical tips for building better applications with AI components.*. Help someone else learn today:.Like, comment, and share so more people can learn too!.

The Takeaway ✈️. Logit bias provides surgical control over AI language patterns by suppressing specific tokens that scream "AI-generated." This simple technique helps your AI writing blend seamlessly with human-authored content.

The Solution: Strategic Logit Bias ✅. A better approach is to use logit bias to suppress the most obvious AI tells like em-dashes and overly formal language. This technique gives you direct control over token-level generation patterns. [Code example - see attached image]. Why.

The Problem ❌. Many developers accept default AI behavior without considering how to suppress common AI tells. This creates challenges that aren't immediately obvious:. [Code example - see attached image]. Why this approach falls short:. - AI Fingerprints: Em-dashes and formal.

🤖"Everyone says my writing sounds like AI wrote it with all the em-dashes?" That's because AI did write it. And those punctuation patterns are dead giveaways. AI models have distinctive linguistic fingerprints, especially around specific tokens like em-dashes and overly formal

You would share information about your AI challenges in a small cohort course if….

Are you having trouble selecting models for your workflows/projects?.

if you want to stop procrastinating, you need to make it smaller:. - work for a smaller amount of time before a break (I have done as short as 3 minutes!!).- do a smaller subpart of the work (it should feel *too* easy). It it really difficult to make progress against large,.

*Part of the "Effective AI Engineering" series - practical tips for building better applications with AI components.*. Help someone else learn today by liking, commenting, and sharing this post!.

The Takeaway ✈️. Gleaning lets AI systems refine their own responses through iterative feedback loops, mimicking the natural revision process that produces better writing. This approach consistently generates higher-quality outputs than single-shot generation.

The Solution: Gleaning ✅. A better approach is to let the AI refine its own responses iteratively. This gleaning technique creates a feedback loop where the AI critiques and improves its own output until it meets your standards. [Code example - see attached image]. Why this.

The Problem ❌. Many developers make single AI calls and hope for the best, starting over completely when responses don't meet requirements. This creates challenges that aren't immediately obvious:. [Code example - see attached image]. Why this approach falls short:. - No.

🔄 Imagine an AI that learns from its mistakes. An AI that doesn’t just generate a response, but refines it. Iteratively. Until it meets your standards. You copy and paste ChatGPT's response into Gmail. It's not quite right. You tap the keyboard as if the right words will

I want to live code more things: what do you most want to see?.

@YouTube

One min folks. Issues with the stream (aka @YouTube is hot garbage for streaming).

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