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Harnessing agentic coding toward autonomous engineering: What we’ve learned so far

A CTO's hard-won perspective on moving AI beyond code generation, building the guardrails, review loops, and text-driven control planes that make autonomous engineering safe, reviewable, and realy.

AI engineering

Apr 1, 2026

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Enterprise search

Apr 14, 2025

T-RAG = RAG + fine-tuning + entity detection

T-RAG = RAG + Fine-Tuning + Entity Detection- Discover T-RAG, a powerful model that combines RAG, fine-tuning, and entity detection for better AI accuracy.

Cobus Greyling

Generative AI

Apr 10, 2025

Beyond Chain-of-Thought LLM reasoning

Beyond Chain-of-Thought LLM Reasoning This approach can be implemented on a prompt level and does not require any dedicated frameworks or pre-processing.

Cobus Greyling

Generative AI

Apr 9, 2025

Comparing human, LLM & LLM-RAG responses

A recent study, focusing on the healthcare & preoperative medicine compared expert human feedback with LLM generation and RAG enhanced responses.

Cobus Greyling

Conversational AI

Apr 8, 2025

Designing conversational UIs that match user intent

In this article I illustrate how to achieve intent alignment by making use of the Kore.ai XO Platform Intent Discovery Tool.

Cobus Greyling

Generative AI

Apr 7, 2025

A benchmark for verifying chain-of-thought

A Chain-of-Thought is only as strong as its weakest link; a recent study from Google Research created a benchmark for Verifiers of Reasoning Chains

Cobus Greyling

Generative AI

Apr 2, 2025

Seven RAG engineering failure points

Explore the seven key failure points in engineering RAG systems, from missing content and retrieval errors to wrong formats and incomplete responses.

Cobus Greyling

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Blog

Harnessing agentic coding toward autonomous engineering: What we’ve learned so far

A CTO's hard-won perspective on moving AI beyond code generation, building the guardrails, review loops, and text-driven control planes that make autonomous engineering safe, reviewable, and realy.

All Articles

T-RAG = RAG + fine-tuning + entity detection

Beyond Chain-of-Thought LLM reasoning

Comparing human, LLM & LLM-RAG responses

Designing conversational UIs that match user intent

A benchmark for verifying chain-of-thought

Seven RAG engineering failure points

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