My LLM kept implementing every method it found, so I added research and specification gates[D]
Summary
An LLM developer added research and specification gates to prevent the model from implementing every method it encounters, improving code generation quality.
Similar Articles
Fence: Specialized SLM Guardrails for LLM Applications
Fence proposes using Small Language Models trained on high-quality synthetic data as specialized guardrails for LLM applications, demonstrating performance gains over prompt-based LLM guardrails.
I spent 40% of my development time preventing an LLM from citing sources wrong. here are the 7 failure modes I found
A developer building an AI legal assistant for a German law firm details seven specific LLM citation failure modes and the prompt-engineering fixes used to meet strict legal citation standards.
Testing Local LLMs in Practice: Code Generation, Quality vs. Speed
The author built a benchmark harness to evaluate local LLMs for autonomous Go code generation, focusing on log parser generation for SIEM pipelines, and published results comparing quality vs. speed.
DSLs Enable Reliable Use of LLMs
This article explores how domain-specific languages (DSLs) improve the reliability and design insight when using LLMs for code generation, emphasizing iterative design and constrained syntax.
DLLG: Dynamic Logit-Level Gating of LLM Experts
DLLG (Dynamic Logit-Level Gating) is a novel framework that dynamically fuses multiple specialized LLMs at the token-level logit space using a lightweight learned gating module, outperforming routing, heuristic ensembling, and parameter-merging baselines across reasoning and code benchmarks. The approach requires only sparse response-level supervision and preserves expert modularity without retraining.