Update content & meta image after context engineering updates (#502)

* Update content & meta image after context engineering updates

* Fix the meta description

Author: bilgeyucel

config.toml

@@ -19,7 +19,7 @@ paginatePath = "/"
 # Defaults used for opengraph metadata if none are specified for the page
 [params]
   title = "Haystack"
-  description = "Create agentic, compound AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications."
+  description = "Create agentic, context engineered AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications."
   images = ["/images/haystack-ogimage.png"]
 
 [permalinks]

content/_index.md

@@ -3,11 +3,11 @@ layout: index
 header: dark
 footer: light
 title: Haystack
-description: Create agentic, compound AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications.
+description: Create agentic, context engineered AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications.
 
 # Hero
 hero:
-  title: The Open Source AI Framework <br/>for Production Ready <br/> RAG & Agents
+  title: The Open Source AI Framework <br/>for Production Ready <br/> Agents, RAG & Context Engineering
   buttons:
     - buttonText: pip install haystack-ai
     - buttonText: Get Started with Haystack
@@ -61,10 +61,10 @@ hero:
   features:
     title: Why Teams Choose Haystack for their AI Workflows
     items:
-      - title: Build Transparent, Customizable AI Systems
+      - title: Build Transparent, Context Engineered AI Systems
         icon: /images/icons/star-icon.svg
         text: |
-          Orchestrate every step of your AI agent, from retrieval to reasoning to tool use.
+          Orchestrate every step of your AI agent, from retrieval to reasoning to memory and tool use.
 
           Haystack’s modular framework gives you full visibility to inspect, debug, and optimize every decision your AI makes.
 
@@ -164,14 +164,14 @@ useCases:
     - title: Advanced<br> RAG
       text: Build highly performant RAG pipelines with a multitude of retrieval and generation strategies. From hybrid retrieval to self-correction loops, Haystack has got you covered.
       icon: /images/icons/use-case-1.svg
-    - title: Agentic<br> Pipelines
-      text: All our LLM generators come with a standard function-calling interface so that your LLM can leverage tools to achieve more. Our pipeline architecture provides branching and looping to support complex agent workflows.
+    - title: AI<br> Agents
+      text: Design production-ready AI agents with standardized tool calling and scalable context engineering. Branching and looping pipelines give you full control over complex, multi-step decision flows.
       icon: /images/icons/use-case-4.svg
     - title: Multimodal<br> AI
-      text: Architect a next generation AI app around all modalities, not just text. Haystack can do tasks like image processing, image generation, and audio transcription too.
+      text: Architect a next generation AI app around all modalities, not just text. Haystack can do tasks like image processing and audio transcription too.
       icon: /images/icons/use-case-1.svg
     - title: Conversational<br> AI
-      text: All of our generators provide a standardized chat interface so that you can focus on building the perfect bot for your users.
+      text: All of our generators provide a standardized interface so that you can focus on building the perfect bot for your users.
       icon: /images/icons/use-case-2.svg
     - title: Content<br> Generation
       text: The flexibility and composability of Haystack’s prompt flow is unparalleled. Leverage our Jinja-2 templates and build a content generation engine that exactly matches your workflow.

content/overview/intro.md

@@ -3,16 +3,16 @@ layout: overview
 header: dark
 footer: dark
 title: What is Haystack?
-desciption: Create agentic, compound AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications.
+desciption: Create agentic, context engineered AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications.
 weight: 1
 toc: true
 ---
 
-[Haystack](https://github.com/deepset-ai/haystack) is an open-source AI orchestration framework built by [deepset](https://www.deepset.ai/) that empowers Python developers to build **real-world, compound, agentic LLM applications**.
+[Haystack](https://github.com/deepset-ai/haystack) is an open-source AI orchestration framework built by [deepset](https://www.deepset.ai/), enabling Python developers to **build production-ready AI agents, multimodal applications, and advanced RAG systems**.
 
-Whether you’re building **autonomous agents**, **multi-modal apps**, or **scalable RAG systems**, Haystack gives you the tools to go from idea to production with ease. Its powerful, modular architecture helps you try out state-of-the-art methods, customize deeply when needed, and stay in control of your stack.
+Built for scalable context engineering, Haystack gives you control over how information moves through your system, from retrieval and tool use to memory and model execution. Haystack structures agents and applications as explicit, modular pipelines composed of retrievers, routers, memory layers, tools, evaluators, and generators. This modular architecture allows each component to be tested, replaced, and improved independently. As a result, you can ship faster and continuously evolve applications in production.
 
-Backed by an inspiring [community](https://haystack.deepset.ai/community) of developers, Haystack has evolved into a **complete and intuitive framework** for LLM-based development.
+Backed by a global developer community and enterprise customer adoption, Haystack has become a trusted framework for building AI applications that operate reliably in complex, mission-critical environments.
 
 ```bash
 pip install -U haystack-ai
@@ -22,47 +22,66 @@ pip install -U haystack-ai
 
 ## Who is Haystack For?
 
-Haystack is for builders of all kinds—whether you’re prototyping your first AI application or deploying sophisticated systems at scale. If you know a bit of Python, you’re ready to start.
+Haystack is built for AI developers and engineers creating AI systems that need to be accurate, modular, and transparent. It supports everything from agent workflows to advanced RAG and multimodal applications.
 
-You can build directly in Python or scale up with enterprise-grade infrastructure on the [Haystack Enterprise Platform](https://www.deepset.ai/products-and-services/deepset-ai-platform). Whatever your level or need, Haystack gives you the power to build, experiment, and launch faster.
+It’s designed for AI systems running in real-world enterprise environments. Haystack integrates with existing architectures, supports governance and compliance requirements, and lets you keep evolving your application as models, techniques, and user needs change.
+
+Run Haystack in your own environment, get [dedicated support](https://www.deepset.ai/products-and-services/haystack-enterprise-starter) or extend it with [a cloud or self-hosted platform](https://www.deepset.ai/products-and-services/haystack-enterprise-platform) to test, deploy, and operate pipelines at scale.
 
 ## Why Developers Love Haystack
 
 Built for production from day one, Haystack is the leading open-source framework for building reliable, flexible, and composable LLM applications from first prototype to full-scale deployment. 
 
+Modern AI systems don’t fail because of the model. They fail because context is not well managed.
+Haystack gives developers full control over how context is retrieved, structured, routed, evaluated, and improved over time.
+
 Here’s why developers choose Haystack:
 
-- **Build flexible pipelines** using Haystack’s built-in components like retrievers, generators, rankers, agent, or [create your own](https://docs.haystack.deepset.ai/docs/custom-components).
-- **Customize with full control** by [adding loops and branches to your pipeline](https://docs.haystack.deepset.ai/docs/pipelines), and tailor data flow to fit your use case.
-- **Integrate smoothly** with [model providers, vector databases, and AI tools](https://haystack.deepset.ai/integrations) in a modular, swappable architecture
-- **Deploy effortlessly** and serve your pipelines as REST APIs or as an MCP Server using with [Hayhooks](https://github.com/deepset-ai/hayhooks).
-- **Monitor and optimize** using built-in [tracing](https://docs.haystack.deepset.ai/docs/tracing), [logging](https://docs.haystack.deepset.ai/docs/logging), and [evaluation](https://docs.haystack.deepset.ai/docs/evaluation) tools for full observability.
-- **Rely on robust engineering** with clean architecture, careful dependency management, and a [breaking change policy](https://docs.haystack.deepset.ai/docs/breaking-change-policy) that ensures no bad surprises.
+**Engineer context, not just prompts**  
+Design explicit pipelines that control retrieval, ranking, memory, tools, and generation so every token sent to the model is intentional and traceable.
+
+**Build flexible, composable systems**  
+Use built-in components like retrievers, generators, rankers, and agents or [create your own](https://docs.haystack.deepset.ai/docs/custom-components). Add loops, branches, and conditional logic to tailor [context flow](https://docs.haystack.deepset.ai/docs/pipelines) to your exact use case.
+
+**Swap models and components without rewriting your system**  
+[Integrate](https://haystack.deepset.ai/integrations) smoothly with model providers, vector databases, and AI tools in a modular architecture built for change and continual improvement.
+
+**Deploy into real environments**  
+Serve pipelines as REST APIs or MCP servers with [Hayhooks](https://github.com/deepset-ai/hayhooks). Run in your own infrastructure and integrate with enterprise architectures.
 
-Whether it’s **agent workflows, retrieval over millions of docs, text-to-SQL or** **PDF parsing,** Haystack scales with your idea without scaling your complexity.
+**Observe and improve continuously**    
+Use [tracing](https://docs.haystack.deepset.ai/docs/tracing), [logging](https://docs.haystack.deepset.ai/docs/logging), and [evaluation](https://docs.haystack.deepset.ai/docs/evaluation) tools to monitor retrieval quality, latency, tool usage, and outputs and iteratively refine your system.
+
+**Build on stable engineering foundations**    
+Clean architecture, careful dependency management, and a [breaking change policy](https://docs.haystack.deepset.ai/docs/breaking-change-policy) that avoids surprises in production.
+
+Whether you're building **agent workflows, retrieval over millions of docs, text-to-SQL pipelines, or document processing flows**, Haystack lets you scale capabilities without increasing system complexity by giving you explicit control over context flow.
 
 ## The Haystack Ecosystem
 
-We’re building the tools to make LLMs truly usable in production—open source and beyond.
+We build tools that make LLM applications usable in production, from open-source foundations to full-scale operational platforms.
 
-- [Haystack](https://github.com/deepset-ai/haystack) → Our core framework for AI orchestration, launched in 2020.
-- [Haystack Enterprise Starter](https://www.deepset.ai/products-and-services/haystack-enterprise) → Expert support, advanced pipeline templates, deployment guides, and early access features for teams needing more support and guidance.
-- [Haystack Enterprise Platform](https://www.deepset.ai/products-and-services/deepset-ai-platform) → An enterprise-ready platform for teams running Gen AI apps in production, with security, governance, and scalability built in.
-- [Haystack Enterprise Platform Trial](https://www.deepset.ai/deepset-studio) → A free visual interface for building pipelines, experimenting with data, and deploying apps for development.
+- [Haystack](https://github.com/deepset-ai/haystack) → Our open-source Python framework for AI orchestration, launched in 2020. Build modular pipelines that control retrieval, agents, tools, and generation with full transparency and flexibility.
+- [Haystack Enterprise Starter](https://www.deepset.ai/products-and-services/haystack-enterprise-starter) → Expert guidance, advanced pipeline templates, deployment playbooks, and early access features for teams that want to move faster with experienced support.
+- [Haystack Enterprise Platform](hhttps://www.deepset.ai/products-and-services/haystack-enterprise-platform) → A production platform for running Gen AI applications at scale with governance, testing, observability, security controls, and infrastructure flexibility designed for real-world enterprise environments. [Try it for free](https://www.deepset.ai/haystack-enterprise-platform-trial).
 
-Recognized as a [2024 Gartner Cool Vendor in AI Engineering](https://landing.deepset.ai/deepset-recognized-as-a-gartner-cool-vendor), we’re proud to serve the community and enterprises alike.
+Recognized as a [Gartner Cool Vendor in AI Engineering](https://landing.deepset.ai/deepset-recognized-as-a-gartner-cool-vendor), we’re proud to support both the global open-source community and enterprises building mission-critical AI systems.
 
 ## Haystack Community
 
-Behind Haystack is a global community of makers, researchers, and tinkerers who believe in the power of open-source collaboration. From first-time contributors to core developers, our community brings a rich mix of backgrounds, interests, and skills.
+Behind Haystack is a global community of developers, researchers, and AI builders who care about making LLM systems reliable in the real world.
+
+From first-time contributors to core maintainers, the community brings deep expertise across retrieval, agents, evaluation, infrastructure, and applied AI. Together, we test ideas, bring them to production-readiness, share implementation patterns, and push the boundaries of what open-source AI systems can do.
+
+Haystack grows through practical collaboration, not just experimentation, but building systems that work.
 
 We’d love to have you involved:
 
 - 🤝 Connect with others and ask questions on [Discord](https://discord.com/invite/xYvH6drSmA)
 - 🧑‍💻 Contribute to the project on [GitHub](https://github.com/deepset-ai/haystack)
 - 🎙️ Join us at [live events, webinars, and office hours](https://lu.ma/haystack)
 - 📺 Catch talks and tutorials on [YouTube](https://www.youtube.com/@haystack_ai)
-- 🐘 Follow along on [X (Twitter)](https://x.com/Haystack_AI) or [Mastodon](https://fosstodon.org/@haystack_ai)
+- 🐦 Follow along on [X (Twitter)](https://x.com/Haystack_AI)
 - 💌 Get our latest news in your inbox via the [Haystack newsletter](https://landing.deepset.ai/haystack-community-updates)
 
 ## Enter the Haystack Open Source Universe

static/images/haystack-ogimage.png

@@ -28,7 +28,7 @@
 
         {{/* Tagline */}}
         <p class="footer-tagline">
-          Building products, technology and solutions for LLM-enabled applications.
+          Build custom AI agents and RAG applications with smart context engineering, powered by open AI orchestration.
         </p>
 
         {{/* Legal links (desktop) */}}