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This podcast describes the Knowledge Augmented Generation (KAG) Framework.

There are 3 main components: KAG-Builder (for offline indexing), KAG-Solver (for hybrid reasoning), and KAG-Model (for optimisation).

The framework leverages Natural Language Understanding (NLU), Natural Language Inference (NLI), and Natural Language Generation (NLG) – core NLP processes – to enable the system to understand, reason with, and generate human-like text. NLU interprets input, NLI establishes logical connections, and NLG produces coherent outputs.

In essence, KAG integrates knowledge construction, reasoning, and model optimisation for advanced text processing.