AI BriefingBuilding Long-Context AI Apps with Jamba
Key point
AI21 Labs has released a Jamba-based long-context course with DeepLearning.AI.
Details
AI21 Labs has released the Build Long-Context AI Apps with Jamba course together with DeepLearning.AI. This hands-on course covers how to design AI agents that need to maintain long context, based on Jamba's hybrid architecture.
It notes that as context grows longer, conventional Transformer computation costs rise sharply, while Mamba is more efficient but has limitations in context understanding and output quality. AI21 explains that Jamba is a structure that combines Transformer's precise attention with Mamba's efficiency, and introduces that it recorded the best effective context length on NVIDIA's independent ruler benchmark.
The course covers the following:
- Building document processing pipelines with AI21SDK
- LangChain integration
- Implementing RAG for knowledge-intensive tasks
- Applying tool-calling for specialized features
The target audience is developers with basic Python skills who are interested in AI agents and large-scale document processing. After completion, learners will be able to deploy Jamba-based agents in real-world environments and build systems that handle documents, conversation history, and knowledge bases all at once.
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