docs(weave): Update intro to reflect product map

docs/docs/concepts.md

@@ -0,0 +1,91 @@
+# Weave concepts and lifecycle
+
+The Weave workflow can be broken into three major stages, organized as a loop:
+
+1. [Exploration](#exploration): Experiment with prompts, pipelines, and initial test cases.
+2. [Systematic Iteration](#systematic-iteration): Build evaluation datasets, compare models, and improve system performance.
+3. [Launch and Learn](#launch-and-learn): Deploy applications, collect feedback, and iteratively refine.
+
+Each stage includes specific substages that connect to core Weave features, as visualized in the diagram.
+
+![Weave usage lifecycle](../static/img/weave-cycle.png)
+
+## Exploration
+
+**Objective**: Early-stage experimentation to explore potential solutions and define tasks.
+
+### Experiment
+- **User Activities**: Experimenting with prompts and pipelines (e.g., Retrieval-Augmented Generation, or RAG).
+- **Related Weave Features**: 
+  - **App Tracing & Debugging**: 🟢 Lightweight tracing for quick insights.
+  - **LLM Playground**: 🔵/🚧 Integrated environment for replayable experimentation.
+  - **Cost Tracking**: 🟢 Understand and optimize resource usage.
+- **Outcome**: Insights into initial LLM behaviors and problem framing.
+
+### Fine-Tune
+- **User Activities**: Fine-tuning frontier models with domain-specific data.
+- **Related Weave Features**:
+  - **Prompt Management & Versioning**: 🔵/🚧 Track and refine prompt iterations.
+  - **Fine-Tune Tracking**: 💡 Monitor training improvements.
+- **Outcome**: Models tailored to specific domains or applications.
+
+---
+
+## Systematic Iteration
+
+**Objective**: Rigorous testing and evaluation to prepare for production.
+
+### Evaluate
+- **User Activities**: Building evaluation datasets and scoring methods.
+- **Related Weave Features**:
+  - **Evaluation Framework**: 🟢/📝 Code- and UI-based framework with intelligent caching.
+  - **Built-in Scorers**: 🔵 Automated scoring for datasets.
+- **Outcome**: Confidence in model accuracy and reliability.
+
+### Compare
+- **User Activities**: Comparing models and techniques.
+- **Related Weave Features**:
+  - **Model Management**: 🟢 Seamlessly organize and compare models.
+  - **Model Comparison Reports**: 🟢 Visualize differences in performance.
+  - **Leaderboards**: 🟢 Highlight top-performing approaches.
+- **Outcome**: Clear selection of the best-performing configurations.
+
+---
+
+## Launch and Learn
+
+**Objective**: Deploy LLM applications, collect feedback, and iterate.
+
+### Deploy
+- **User Activities**: Deploying the model/application.
+- **Related Weave Features**:
+  - **Production Tracing & Plotting**: 🟢 Monitor real-world behavior.
+  - **Guardrails and Alerts**: 🚧 Proactively identify issues in production.
+- **Outcome**: Applications ready for user interaction.
+
+### Enrich
+- **User Activities**: Collecting live user feedback and production data.
+- **Related Weave Features**:
+  - **Dataset Enrichment**: 🚧 Enhance evaluation datasets with production insights.
+  - **User Feedback Collection**: 🟢 Record and analyze interactions.
+- **Outcome**: Improved datasets and understanding of real-world use cases.
+
+### Fine-Tune
+- **User Activities**: Refining models based on production data.
+- **Related Weave Features**:
+  - **Fine-Tuning with Production Data**: 💡 Close the loop with improved performance.
+- **Outcome**: Enhanced model accuracy and responsiveness.
+
+---
+
+## Cross-Stage Foundations
+
+Weave includes foundational features that enhance every stage of the workflow:
+
+- **Multi-Client Support**: 🟢 Python, TypeScript, HTTP APIs, and more.
+- **Data Export**: 🟢 Export system data for external analysis.
+- **Saved Views**: 🚧 Share analytics and evaluations.
+- **Custom Mods**: 🚧 Build custom apps using Weave as a database.
+- **W&B Integration**: 🚧 Connect model development with evaluations and workflows.
+
+---

docs/docs/introduction.md

@@ -2,24 +2,16 @@
 slug: /
 ---
 
-# Introduction
+# Introduction to Weave
 
-**Weave** is a lightweight toolkit for tracking and evaluating LLM applications, built by Weights & Biases.
+Weave is a tool for exploring, evaluating, and deploying LLM-based applications, built by Weights & Biases (W&B). Designed for flexibility and scalability, Weave supports every stage of the LLM application development workflow, from initial experimentation to systematic iteration and deployment.
 
-Our goal is to bring rigor, best-practices, and composability to the inherently experimental process of developing AI applications, without introducing cognitive overhead.
+## Get started
 
-**[Get started](/quickstart)** by decorating Python functions with `@weave.op()`.
+Do the quickstart...
 
-![Weave Hero](../static/img/weave-hero.png)
-
-Seriously, try the 🍪 **[quickstart](/quickstart)** 🍪 or <a class="vertical-align-colab-button" target="\_blank" href="http://wandb.me/weave_colab" onClick={()=>{window.analytics?.track("Weave Docs: Quickstart colab clicked")}}><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
-
-You can use Weave to:
-
-- Log and debug language model inputs, outputs, and traces
-- Build rigorous, apples-to-apples evaluations for language model use cases
-- Organize all the information generated across the LLM workflow, from experimentation to evaluations to production
-
-## What's next?
-
-Try the [Quickstart](/quickstart) to see Weave in action.
+- Other pages that we want to link users to...
+- Reference
+- Cookbooks
+- SDK
+- ...