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docs/docs/index.md

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 DSPy is the open-source framework for _programming—rather than prompting—language models_. It allows you to build modular AI systems and to iterate fast on your AI system design. To do this, DSPy provides abstractions and algorithms for **optimizing the prompts and weights** in any LM program you're building, from simple classifiers to sophisticated RAG pipelines and Agent loops.
 
-DSPy stands for Declarative Self-improving Python. Instead of writing brittle prompts for a specific LM, you write portable compositional _Python code_ and use DSPy to **teach your LM to deliver high-quality outputs** more reliably. This [recent lecture](https://www.youtube.com/watch?v=JEMYuzrKLUw) is a good conceptual introduction. Our [Discord server](https://discord.gg/XCGy2WDCQB) is a great place to meet the community, seek help, or start contributing.
+DSPy stands for Declarative Self-improving Python. Instead of writing brittle prompts for a specific LM, you write portable compositional _Python code_ and use DSPy to **teach your LM to deliver high-quality outputs** more reliably. This [recent lecture](https://www.youtube.com/watch?v=JEMYuzrKLUw) is a good conceptual introduction. Our [GitHub repo](https://github.com/stanfordnlp/dspy) and [Discord server](https://discord.gg/XCGy2WDCQB) are great places to meet the community, seek help, or start contributing.
 
 
 !!! info "Getting Started I: Install DSPy and set up your LM"
@@ -113,9 +113,9 @@ DSPy stands for Declarative Self-improving Python. Instead of writing brittle pr
      ``` 
 
 
-## 1) **Modules** express portable, natural-language-typed behavior.
+## 1) **Modules** express portable, _natural-language-typed_ behavior.
 
-To build reliable AI systems, you need to iterate fast. Especially on how to break your problem down into modular LM components. But it's really hard to iterate when your system involves maintaining multiple long prompt strings: the typical way of using LMs forces you to tinker with each component's messy prompts or synthetic data _every time you change the model, the metrics, or parts of the pipeline_ or when you just want to try a new technique. Having built over a dozen state-of-the-art compound LM systems over the past five years, we learned this the hard way—and we built DSPy so you don't have to.
+To build reliable AI systems, you need to iterate fast. Especially on how to break your problem down into modular LM components. But the typical way of using LMs makes it really hard to iterate fast: maintaining multiple long prompt strings often forces you to tinker with each component's messy prompts (or, worse, synthetic data) _every time you change the model, the metrics, or parts of the pipeline_ or when you just want to try a new technique. Having built over a dozen state-of-the-art compound LM systems over the past five years, we learned this the hard way—and we built DSPy so you don't have to.
 
 DSPy shifts your focus from tinkering with prompt strings to **programming with structured, declarative, and natural-language-typed modules**. For every component in your AI system, you define a _signature_, which specifies input/output types and behavior, and a _module_, which specifies an inference-time strategy for using your LM well. DSPy handles expanding your signatures into prompts and parsing your typed outputs, so you can write ergonomic, portable, and optimizable AI systems.
 
@@ -234,7 +234,7 @@ DSPy shifts your focus from tinkering with prompt strings to **programming with
 
 ## 2) **Optimizers** tune the prompts and weights of your Modules.
 
-The goal of DSPy is to provide you with the tools to compile your high-level code into the low-level computations, prompts, or weight updates that **align your LM with your program’s structure and metrics**.
+The goal of DSPy is to provide you with the tools to compile high-level, _natural-language-typed_ code into low-level computations, prompts, or weight updates that **align your LM with your program’s structure and metrics**.
 
 Given a few tens or hundreds of representative _inputs_ of your task and a _metric_ that can measure the quality of your system's outputs, you can use a DSPy optimizer. Different optimizers in DSPy will tune your program's quality by **synthesizing good few-shot examples** for every module, like `dspy.BootstrapRS`,<sup>[1](https://arxiv.org/abs/2310.03714)</sup> **proposing and intelligently exploring better natural-language instructions** for every prompt, like `dspy.MIPROv2`,<sup>[2](https://arxiv.org/abs/2406.11695)</sup> and **building datasets for your modules and using them to finetune the LM weights** in your system, like `dspy.BootstrapFinetune`.<sup>[3](https://arxiv.org/abs/2407.10930)</sup>