In my experience, navigating expectations around AI algorithm limitations requires transparent communication and a focus on practical outcomes. What I’ve usually seen work well is explaining the algorithm's strengths and limitations clearly, helping the team understand what it can and cannot achieve. I believe setting realistic performance benchmarks and highlighting incremental improvements can keep the team motivated, while exploring potential workarounds or optimizations. Additionally, fostering a mindset of continuous learning and experimentation allows the team to embrace challenges and find creative solutions.

🤣 Lot's of AI written answers in the comments here I shouldn't be surprised 👀 here's a manually written answer, old-school, so you better click insightful on this 🙏🏼 Set up a culture of "have fun" and experiment 🧪 Try and break the algorithm in many ways, figure out what the limitations in it are, and experiment/test ways that you can get around those limitations. I found this strategy works with things like ChatGPT, Claude, Perplexity and Leonardo AI. You could also set challenges! (Not goals or targets) i.e. first person to get around this limitation wins a 🍪or something else, maybe dangle a🥕

To effectively manage your team's expectations regarding AI algorithm limitations, begin by fostering transparency around the AI’s capabilities and constraints. Set clear, realistic goals that align with the technology's current potential, and emphasize the importance of iterative improvement. Encourage a collaborative mindset where challenges are seen as opportunities for innovation. By regularly communicating progress, engaging in problem-solving discussions, and providing a roadmap for overcoming obstacles, you can keep morale high while guiding your team toward achieving results within the system's limits.

When you reach the limit of a Generative AI model such as Llama 3, Gemini or GPT-4o, there are several pragmatic solutions : 1.Try to take prompt engineering as far as possible with well built inputs. 2.Then try Retrieval Augmented Generation (RAG). You can build: A) Naive RAG by directly retrieving rawdocuments related to the prompt /input and adding them to the input. B) Advanced RAG by embedding and indexing a knowledge base in a vector store and then retrieve data to fit and augment the input /prompt. C) Modular RAG : combine methods A and B with flexibility. 3. Try fine - tuning with large well prepared datasets. 4. Try several models if necessary. Last but not least, remain optimistic, determined, and resilient! 😊