From 5f9c15692a8d3a2df46bcd851871720fca2f602e Mon Sep 17 00:00:00 2001
From: Courtland Leer <93223786+courtlandleer@users.noreply.github.com>
Date: Fri, 23 Feb 2024 15:26:48 -0500
Subject: [PATCH] more transition copy

---
 content/blog/User State is State of the Art.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/content/blog/User State is State of the Art.md b/content/blog/User State is State of the Art.md
index efd5cd3fe..1a59ceb18 100644
--- a/content/blog/User State is State of the Art.md	
+++ b/content/blog/User State is State of the Art.md	
@@ -71,7 +71,9 @@ We can (and should) even allow our AI apps the agency to decide what elements of
 
 ## A DSPy Demo for Honcho
 
-Today we're releasing a demo to be used with Honcho that begins to tease out some technical, concrete approaches to all these heady concepts. With enough message and session data stored with Honcho, we can start to learn and optimize for common states your users are in while using your app or agent. Is Alice in research mode? Is Bob looking for some companionship? Maybe today, Carol just wants to get shit done, or Charlie needs delicate treatment because he's pissed.
+Today we're releasing a demo to be used with Honcho that begins to tease out some technical, concrete approaches to all these heady concepts--first steps at imbuing our tools with the right meta-methods. 
+
+With enough message and session data stored with Honcho, we can start to learn and optimize for common states your users are in while using your app or agent. Is Alice in research mode? Is Bob looking for some companionship? Maybe today, Carol just wants to get shit done, or Charlie needs delicate treatment because he's pissed.
 
 Recently, the DSPy framework has been [catching on](https://x.com/lateinteraction/status/1758734906648375633?s=20), garnering a ton of attention. It's helped people improve RAG performance, reduce costs, and [_generally feel better_](https://x.com/lateinteraction/status/1757190669196755259?s=20) about building LLM applications. It aims to emulate [PyTorch](https://x.com/lateinteraction/status/1758952248124776925?s=20) in its design philosophy, making LLM application development much more like _programming_ as opposed to manipulating free-form text strings (prompting). There are built-in optimizers for _learning_ the optimal prompt given a metric.