update docs

Author: okhat

docs/docs/index.md

@@ -117,9 +117,9 @@ DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, y
 
 ## 1) **Modules** help you describe AI behavior as _code_, not strings.
 
-To build reliable AI systems, you must iterate fast. But maintaining prompts makes that hard: it forces you to tinker with strings or data _every time you change your LM, metrics, or pipeline_. Having built over a dozen best-in-class compound LM systems since 2020, we learned this the hard way—and so built DSPy to decouple defining LM systems from messy incidental choices about specific LMs or prompting strategies.
+To build reliable AI systems, you must iterate fast. But maintaining prompts makes that hard: it forces you to tinker with strings or data _every time you change your LM, metrics, or pipeline_. Having built over a dozen best-in-class compound LM systems since 2020, we learned this the hard way—and so built DSPy to decouple AI system design from messy incidental choices about specific LMs or prompting strategies.
 
-DSPy shifts your focus from tinkering with prompt strings to **programming with structured and declarative natural-language modules**. For every AI component in your system, you specify input/output behavior as a _signature_ and select a _module_ to assign a strategy for invoking your LM. DSPy expands your signatures into prompts and parses your typed outputs, so you can write ergonomic, portable, and optimizable AI systems.
+DSPy shifts your focus from tinkering with prompt strings to **programming with structured and declarative natural-language modules**. For every AI component in your system, you specify input/output behavior as a _signature_ and select a _module_ to assign a strategy for invoking your LM. DSPy expands your signatures into prompts and parses your typed outputs, so you can compose different modules together into ergonomic, portable, and optimizable AI systems.
 
 
 !!! info "Getting Started II: Build DSPy modules for various tasks"
@@ -370,6 +370,6 @@ BootstrapFS on MATH with a tiny LM like Llama-3.2 with Ollama (maybe with a big
 
 Compared to monolithic LMs, DSPy's modular paradigm enables a large community to improve the compositional architectures, inference-time strategies, and optimizers for LM programs in an open, distributed way. This gives DSPy users more control, helps them iterate much faster, and allows their programs to get better over time by applying the latest optimizers or modules.
 
-The DSPy research effort started at Stanford NLP in Feb 2022, building on what we learned from developing early [compound LM systems](https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/) like [ColBERT-QA](https://arxiv.org/abs/2007.00814), [Baleen](https://arxiv.org/abs/2101.00436), and [Hindsight](https://arxiv.org/abs/2110.07752). The first version was released as [DSP](https://arxiv.org/abs/2212.14024) in Dec 2022 and evolved by Oct 2023 into [DSPy](https://arxiv.org/abs/2310.03714). Thanks to [250 contributors](https://github.com/stanfordnlp/dspy/graphs/contributors), DSPy has introduced tens of thousands of people to building and optimizing modular LM programs.
+The DSPy research effort started at Stanford NLP in Feb 2022, building on what we had learned from developing early [compound LM systems](https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/) like [ColBERT-QA](https://arxiv.org/abs/2007.00814), [Baleen](https://arxiv.org/abs/2101.00436), and [Hindsight](https://arxiv.org/abs/2110.07752). The first version was released as [DSP](https://arxiv.org/abs/2212.14024) in Dec 2022 and evolved by Oct 2023 into [DSPy](https://arxiv.org/abs/2310.03714). Thanks to [250 contributors](https://github.com/stanfordnlp/dspy/graphs/contributors), DSPy has introduced tens of thousands of people to building and optimizing modular LM programs.
 
 Since then, DSPy's community has produced a large body of work on optimizers, like [MIPROv2](https://arxiv.org/abs/2406.11695), [BetterTogether](https://arxiv.org/abs/2407.10930), and [LeReT](https://arxiv.org/abs/2410.23214), on program architectures, like [STORM](https://arxiv.org/abs/2402.14207), [IReRa](https://arxiv.org/abs/2401.12178), and [DSPy Assertions](https://arxiv.org/abs/2312.13382), and on successful applications to new problems, like [PAPILLON](https://arxiv.org/abs/2410.17127), [PATH](https://arxiv.org/abs/2406.11706), [WangLab@MEDIQA](https://arxiv.org/abs/2404.14544), [UMD's Prompting Case Study](https://arxiv.org/abs/2406.06608), and [Haize's Red-Teaming Program](https://blog.haizelabs.com/posts/dspy/), in addition to many open-source projects, production applications, and other [use cases](/dspy-usecases/).