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Top 10 generative AI articles, tutorials, and learning paths in 2025
Discover the most popular content for 2025's hottest topic: generative AI
Generative AI surged from hype to hands-on reality in 2025, shifting the developer conversation toward practical architectures, smarter workflows, and production-ready AI systems. Whether it was fine-tuning enterprise models, orchestrating multi agent workflows, or squeezing more accuracy out of RAG pipelines, developers pushed the boundaries of what’s possible.
In this roundup, we highlight the top 10 articles, tutorials, and learning paths that developers like you turned to for practical guidance, hands-on skills, and a deeper understanding of where enterprise-grade generative AI is heading next.
#10 Deploying MCP Tools on watsonx Orchestrate by using ContextForge MCP Gateway
In this tutorial, Deploying MCP Tools on watsonx Orchestrate by using ContextForge MCP Gateway, you learn how to expose MCP tools on the MCP Gateway, which acts as a bridge between your agents and the real world of services they depend on. MCP Gateway allows you to manage and secure your tools in a centralized manner, making them accessible to multiple agents and workflows without requiring custom glue code for each integration. By using the MCP Gateway, you focused on designing the workflows and let the MCP Gateway handle the underlying complexities.
Try watsonx Orchestrate for yourself by signing up for your free trial.
Explore more content on IBM Developer about watsonx Orchestrate.
#9 AgentOps in watsonx Orchestrate: Observability for Agents with Langfuse and IBM Telemetry
In this tutorial, AgentOps in watsonx Orchestrate: Observability for Agents with Langfuse and IBM Telemetry, you learn how to add observability to agents on watsonx Orchestrate using IBM Telemetry and Langfuse. In short, AgentOps and LLM Observability treat AI agents as production systems with the same rigor as any critical service. Without this, scaling AI in the enterprise is risky and hard to sustain.
Try watsonx Orchestrate for yourself by signing up for your free trial.
Explore more content on IBM Developer about watsonx Orchestrate.
#8 Fine-tuning IBM Granite language models for enterprise applications using Red Hat Enterprise Linux AI
In this tutorial, Fine-tuning IBM Granite language models for enterprise applications using Red Hat Enterprise Linux AI, you learn how to customize a model for your enterprise use case using Red Hat Enterprise Linux AI (RHEL AI) on IBM Cloud. You are guided through the process of downloading, configuring, initializing, and verifying resources, as well as customizing the taxonomy, generating synthetic data, training the model, and evaluating the new custom model.
#7 Build a multilingual language detection and translation system using IBM watsonx.ai
In this article, Build a multilingual language detection and translation system using IBM watsonx.ai, you learn how to build a multilingual language detection and translation system using the IBM watsonx.ai platform. By combining pretrained models with carefully designed prompts, you can create an LLM-powered application that can accurately detect languages and translate text across multiple languages.
Try watsonx.ai for yourself by signing up for a free trial.
Explore more content on IBM Developer about watsonx.ai.
#6 Build an AI agent with Langflow, Granite 4.0 models, and watsonx Orchestrate
In this tutorial, Build an AI agent with Langflow, Granite 4.0 models, and watsonx Orchestrate, you learn how to use Langflow, IBM Granite 4.0, and watsonx Orchestrate to build a governed production-ready AI agent.
Try watsonx Orchestrate for yourself by signing up for your free trial.
Explore more content on IBM Developer about watsonx Orchestrate.
#5 Reimagining workflows with agentic AI
In this article, Reimagining workflows with agentic AI, you learn about the transformative potential of AI agentic workflows in financial decision-making use cases such as the personal loan origination process. By leveraging agents with access to domain-specific knowledge and tools for credit scoring and risk analysis, this sample showcased how AI can help to enhance the efficiency and responsiveness of financial services. The article also examined the CrewAI framework's adaptability in implementing agentic workflows without writing large amounts of code.
In related content, you can use this comprehensive guide that compares three AI frameworks) (CrewAI, LangGraph, and BeeAI) to help you understand when it's best to use CrewAI for your agentic workflows. Then, explore more practical examples of implementing AI agents with the three AI frameworks.
Ready to give CrewAI a try? Check out this tutorial, Build smart AI agents by integrating CrewAI and watsonx.
#4 SQL evaluation framework for accurate query assessment
In this article, SQL evaluation framework for accurate query assessment, you learn about the SQL-Eval framework that is used to evaluate the correctness of SQL queries generated by large language models (LLMs), such as OpenAI, AWS Bedrock, Gemini, MLX, Mixtral, Anthropic, and more.
#3 Enhancing RAG performance with smart chunking strategies
In this article, Enhancing RAG performance with smart chunking strategies, you learn how to strike a balance between maintaining contextual relevance and ensuring computational efficiency whether you use semantic chunking for documents or timestamp-based chunking for conversational data.
Learn more about the agentic RAG architecture in this article, Building an agentic RAG pipeline. The agentic RAG architecture offers domain specialization, hybrid data architecture, intelligent orchestration, and quality control.
Explore more content on IBM Developer about RAG.
#2 Get started with Data Prep Kit (DPK)
In this learning path, Get started with Data Prep Kit (DPK), you learn how DPK helps you build LLM applications by simplifying the data preparation part of building AI workloads. Through hands-on tutorials, you learn how to prepare data for fine-tuning LLMs, for building a RAG pipeline, and for an agentic workflow.
Explore more content on IBM Developer about Data Prep Kit.
#1 Get started with watsonx Orchestrate
In this learning path, Get started with watsonx Orchestrate, you gain an understanding and working knowledge of watsonx Orchestrate. After introducing multi-agent orchestration using watsonx Orchestrate, it provides hands-on guides for developing agents with no code or by using the Agent Development Kit (ADK).
With this foundational knowlege, developers can now extend their agentic AI skills with these additional watsonx Orchestrate learning paths:
- Multi-agent orchestration with watsonx Orchestrate using technologies such as Langflow, IBM Granite models, and the AI Gateway.
- Agentic standards with watsonx Orchestrate with technologies such as MCP, A2A, ContextForge MCP Gateway, and OpenAI API.
- AgentOps with watsonx Orchestrate using technologies such as Langfuse and IBM Telemetry.
Try watsonx Orchestrate for yourself by signing up for your free trial.
Explore more content on IBM Developer about watsonx Orchestrate.
Want more?
If 2025 was the year developers mastered generative AI, it was because they dove deep into agentic systems powered by watsonx Orchestrate, RAG, CrewAI, LangChain, Langflow, Granite, MCP, and watsonx.ai.
But this momentum is only the beginning. New architectures, smarter automation, and even more capable AI tooling are already on the horizon. Stick around, because 2026 is shaping up to be the year generative AI gets even bolder, faster, and far more fun to build with.