From Prompt Engineering to Flow Engineering: 6 More AI Breakthroughs to Expect in 2024

Reflecting on the predictions made about AI breakthroughs in 2023, the blog post revisits the challenges and developments in large language models (LLMs) and anticipates future advancements for 2024. In 2023, noticeable strides were made in addressing content hallucinations in LLMs through Retrieval Augmented Generation (RAG) systems, enhancing the grounding of information in these models. Tools like LangChain and LlamaIndex gained traction, facilitating connections between LLMs and external data sources, while increasing context sizes in models like Google's Gemini expanded memory capacities, albeit with challenges in effective utilization. The cost of LLM usage has significantly decreased, with open-source models proving more economical than closed-source alternatives, driven by innovations in model efficiency. Fine-tuning has shifted focus towards enhancing model capabilities in specialized domains, supported by techniques like LoRA, while AI alignment efforts have seen global initiatives for ethical regulation. A paradigm shift from "Prompt Engineering" to "Flow Engineering" is emerging, emphasizing iterative processes over single-step prompt modifications to improve AI reasoning, marking a significant development for the coming year.