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  (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.