Glossary

Model chaining

What is Model chaining?

Model chaining means connecting several AI models or AI steps in sequence so that the output of one step becomes the input of the next. Complex tasks can thus be broken down into manageable sub-tasks that can be checked and improved individually. Example: one step summarises a customer enquiry, a second assigns it to a department and a third drafts the reply.

How KI.ordo helps

In KI.ordo flows, AI steps, agent queries, AI routers and sub-flows can be chained without programming, with every step logged.

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Topics

orchestration
flows
prompting

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Related terms

Recursive prompting

Recursive prompting refers to techniques in which a language model's output is fed back in as new input – for instance to have a draft reviewed and revised, or to break a large task down step by step into subtasks. For example, a draft proposal is produced first, the model then assesses it against a checklist, and finally it is improved. This often raises quality but costs extra processing time and needs a clear stopping criterion.

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revision
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LangChain

LangChain is an open-source framework for Python and JavaScript that developers use to build applications based on language models. It provides unified interfaces to many model providers as well as building blocks for tool calling, agents, document processing and retrieval-augmented generation. Since version 1.0 (October 2025), LangChain's agents are built on LangGraph. Typical uses include chatbots that search internal documents and agents that call external services.

Framework
Development
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Multi-hop reasoning

Multi-hop reasoning is an AI system’s ability to answer a question through several successive inferences, often combining information from different sources. Example: “Which department is headed by the person responsible for project X?” first requires identifying the responsible person and then their department. Such questions are demanding for search and RAG systems because every intermediate step must be found and linked correctly.

reasoning
question answering
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AI Automation System

An AI automation system combines traditional process automation with AI capabilities such as understanding text, classification or decision support. This makes it possible to automate workflows with unstructured inputs too, such as emails, forms or enquiries. A typical setup is a chain of trigger, processing steps, conditions and output, with human approval at critical points. Example: incoming enquiries are automatically categorised, summarised and forwarded to the right department.

Automation
Processes
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