The AI shift from prompt engineering to flow engineering
The IT industry is busy working out how to develop, program with and exploit Artificial Intelligence (AI) in our enterprise applications. Key among the techniques being used to create, coalesce and connect AI is prompt engineering i.e. the process of designing detailed input formats (characters, words, phrases, symbols etc.) and channels for AI data so that it produces the optimal output for human interaction and use in any given situation. But just as we’re getting our heads around prompt engineering, some argue that we need to also embrace flow engineering as well, so what is this second technique and why will it matter to our new AI universe?
We know that prompt engineering requires developers to use precise wording and structuring when generating code. While well-crafted prompts are widely agreed to significantly enhance Large Language Model (LLM) performance at code generation, software application developers are more likely to get something close to what they want rather than exactly what they want and not something entirely useful.
This ‘close-almost’ insight is perhaps the most insightful additional caveat and clarification we can get in the current AI engineering discussion as it now plays out. The proposition itself comes from Itamar Friedman, CEO and co-founder of Tel Aviv-based CodiumAI, an organization known for its code testing platform that also offers AI code completion, search and chat functionally. He suggests that the degree of sensitivity to minor variations in phrasing at the prompt engineering level poses a problem for developers and, further, that is a sign that relying on prompt engineering alone isn’t ideal.
