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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

Seven RAG Engineering Failure Points Retrieval-Augmented Generation (RAG) systems remains a compelling solution to the challenge of relevant up-to-date reference data at inference time. Integrating retrieval mechanisms with the generative capabilities of LLMs & RAG systems can synthesise accurate, up-to-date and contextually relevant information.

UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue. Considerable development has taken place in the area of RAG, especially in adding structure and multi-document approaches.UniMS-RAG: Unified Multi-Source RAG for Personalised Dialogue. Considerable development has taken place in the area of RAG, especially in adding structure and multi-document approaches.

Chain-of-Symbol Prompting (CoS) For Large Language Models LLMs need to understand a virtual spatial environment described through natural language while planning & achieving defined goals in the environment.

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Traditional CoT comes at a cost of increased output token usage, CCoT prompting is a prompt-engineering technique which is aimed at reducing LLM response verbosity & inference time.