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When rapid engineering first emerged as a mainstream workflow for data and machine learning professionals, it seemed to generate two common (and somewhat opposing) viewpoints.
In the wake of ChatGPT's splashy arrival, some commentators declared it an essential task that would soon be taken over by entire product and ML teams; Six-figure job offers for fast engineers soon followed. At the same time, skeptics argued that it was little more than an intermediary approach to fill gaps in current LLM skills and that, as the performance of the models improved, the need for specialized motivational knowledge would dissipate.
Nearly two years later, both sides appear to have made valid arguments. Rapid engineering is still very present among us; continues to evolve as a practice, with a growing number of tools and techniques supporting practitioners' interactions with powerful models. However, it is also clear that as the ecosystem matures, optimization cues could become less of a specialized skill than a way of thinking and problem-solving embedded across a broad spectrum of professional activities.
To help you assess the current state of rapid engineering, catch up on the latest approaches, and look toward the future of the field, we've compiled some of our strongest recent articles on the topic. Enjoy your reading!
- Introduction to Domain Adaptation: Motivation, Options, Tradeoffs
For anyone taking their first steps working practically with LLM, the three-part series is a great place to start exploring the different approaches to making these massive, unwieldy and occasionally unpredictable models produce reliable results. The first part, in particular, does a great job of presenting rapid engineering: why it is necessary, how it works, and what trade-offs it forces us to consider. - I obtained a certification in ai. This is what he taught me about rapid engineering.
“Rapid engineering is a simple concept. It is simply a way of asking the LLM to complete a task by providing instructions.” Writing from the perspective of an experienced software developer who wants to stay up to date with the latest industry trends, we are guided through the experience of branching out into the sometimes counterintuitive ways humans and models interact. - Rapid engineering automation with DSPy and Haystack
Many ML professionals who have already tried the prompts quickly realize that there is a batch There is a lot of room for rationalization and optimization when it comes to rapid design and execution. We recently shared a clear, step-by-step tutorial, focused on the open source DSPy framework, for anyone looking to automate important parts of this workflow.
- Understanding techniques to solve GenAI challenges
We tend to focus on the nitty-gritty of rapid engineering implementation, but like other LLM optimization techniques, it also raises a whole set of questions for product and business stakeholders. The new article is a helpful overview that does a great job of offering “guidance on when to consider different approaches and how to combine them for the best results.” - Streamline your indications to reduce LLM costs and latency
Once you’ve established a functional cue engineering system, you can start focusing on ways to make it more efficient and resource-efficient. For practical advice on how to move in that direction, be sure to check out our five tips for optimizing token use in your cues (but without sacrificing accuracy). - From rapid engineering to agent engineering
For an incisive reflection on where this field might be headed in the near future, we hope you'll check out the high-level analysis of: “it seems necessary to begin the transition from rapid engineering to something broader, also known as agent engineering, and establish the appropriate conditions.” frameworks, methodologies and mental models to design them effectively.”
