The new self-ask prompting technique [Paper] lets the LLM (Large Language Model) formulate sub-questions to a given complex question, answers these simpler questions and finally combines the answers to the answer of the complex question. This technique already improves over chain of thought (CoT) prompting [1][2][3] (but no comparison to self-consistency) . However, with a few lines of source code [GitHub], it is also possible to let the simpler questions be answered by a Google search. In this way also questions relying on facts beyond the training corpus of the LLM can be answered!

Meanwhile people also started to combine LLMs not just with a Google search but also with a Python interpreter. Key tool in this context that allows to build up chains of actions is the Python package LangChain that also already integrates the self-ask technique. See also Dust for reproducing OpenAI’s WebGPT via advanced prompting and Everyprompt for parameterized text prompts within a playground application.