  We utilise the power of Large Language Models (LLMs), in particular GPT4, to
be prompt engineered into performing an arbitrary task. Here, we give the model
some human priors via text, along with some typical procedures for solving the
ARC tasks, and ask it to generate the i) broad description of the input-output
relation, ii) detailed steps of the input-output mapping, iii) use the detailed
steps to perform manipulation on the test input and derive the test output. The
current GPT3.5/GPT4 prompt solves 2 out of 4 tested small ARC challenges (those
with small grids of 8x8 and below). With tweaks to the prompt to make it more
specific for the use case, it can solve more. We posit that when scaled to a
multi-agent system with usage of past memory and equipped with an image
interpretation tool via Visual Question Answering, we may actually be able to
solve the majority of the ARC challenge
