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.”

Aliyun Bailian (2): The Qwen LLM API in Production

Thu, 26 Feb 2026 09:00:00 +0000

This article in the series covers most of the production wins. While the other models are interesting, the LLMs are what every product I’ve shipped on Bailian calls every minute of every day. The official Qwen API reference is dense and complete; this article is the readable companion that guides you through it.

Pick the right Qwen variant for the workload#

The Qwen family is large. Some teams overspend by defaulting to qwen-max everywhere; others underspend on quality by defaulting to qwen-turbo. The right answer is “match variant to job”:

Function-Calling on Chen Kai Blog

LLM Engineering (7): Function Calling and Tool Use

Aliyun Bailian (2): The Qwen LLM API in Production

Pick the right Qwen variant for the workload#