LLM Engineering (7): Function Calling and Tool Use

Thu, 02 Apr 2026 09:00:00 +0000

Function calling connects an LLM to the world outside its weights. It combines chat-template details (Chapter 2 ), structured-output kernels (Chapter 5 ), and prompt engineering (Chapter 9 ). This chapter explores what happens under the hood, the guarantees you can rely on, and the agent-loop patterns that handle real workloads.

The intellectual lineage matters. Tool use as an LLM capability traces back to two near-simultaneous papers in 2022: MRKL Systems (Karpas et al., AI21) which proposed expert-routing among neuro-symbolic modules, and ReAct (Yao et al., 2022 ) which interleaved chain-of-thought reasoning with tool actions. Toolformer (Schick et al., 2023 ) showed self-supervised teaching of tool use, generating training data by having a model insert tool-call markers into existing text. By 2024 every frontier model had post-training data structured around the tool-use format, and tool calling moved from “research demo” to “API feature.”

NLP (12): Frontiers and Practical Applications

Tue, 25 Nov 2025 09:00:00 +0000

We have spent eleven chapters climbing from raw text to multimodal foundation models. This twelfth and final chapter sits at the frontier and at the runway. It is where research stops being a paper and starts being a service: an LLM that calls tools, writes and debugs code, reasons through hundred-step problems, ingests a 200K-token contract, and serves a thousand concurrent users behind a FastAPI endpoint with p95 latency under 300 ms.

Agents on Chen Kai Blog

LLM Engineering (7): Function Calling and Tool Use

NLP (12): Frontiers and Practical Applications