STCMastery-Meta-Prompting-250924
ethnography | sO'nografi | noun the scientific description of peoples and cultures with their customs, habits, and mutual differences. Oxford Dictionary of English eth•nog•ra•phy | eTH'nägrafe | noun the scientific description of the customs of individual peoples and cultures. New Oxford American Dictionary
Ethnography, the scientific study of peoples and cultures, is increasingly important in prompt engineering for artificial intelligence. Just as ethnographers document customs and habits, prompt engineers craft detailed prompts to guide AI models in understanding and generating human-like responses. This process involves advanced techniques like métaprompting, enabling large language models to self-improve or create specialised prompts. Companies exemplify this by sharing intricate service client prompts, showcasing the depth of this field. Furthermore, startups in vertical AI employ forward deployed engineering, working closely with clients to integrate specific contexts and achieve precise evaluations, seen as valuable intellectual property. The discussion also highlights the varying “personalities” of AI models in applying guidelines, underscoring the nuanced approach required in both ethnography and prompt engineering.
Ethnography, the scientific study of peoples and cultures, becomes increasingly important in the realm of prompt engineering for artificial intelligence. Just as ethnographers meticulously document customs and habits, prompt engineers craft detailed prompts to guide AI models in understanding and generating human-like responses. This process, akin to the early days of coding, involves advanced techniques like métaprompting, which enables large language models to self-improve or create specialised prompts. Companies like Parahelp exemplify this by sharing intricate service client prompts, showcasing the depth of this field. Furthermore, startups in vertical AI employ forward deployed engineering, working closely with clients to integrate specific contexts and achieve precise evaluations, which are seen as valuable intellectual property. The discussion also highlights the varying “personalities” of AI models, such as the rigour of GPT-4 and the flexibility of Gemini 2.5 Pro, in applying guidelines, underscoring the nuanced approach required in both ethnography and prompt engineering.
La source est une retranscription d'une discussion sur le sujet du prompt engineering pour les agents d'intelligence artificielle, qui est comparé aux débuts du codage et à la gestion de personnel. Les experts discutent des techniques avancées comme le métaprompting, qui permet aux modèles de langage de grande taille (LLM) de s'améliorer eux-mêmes ou de générer des invites plus spécialisées. Un exemple concret est fourni avec l'entreprise Parahelp, qui a généreusement partagé son invite d'agent de service client incroyablement détaillée. Une grande partie de la conversation est consacrée à la façon dont les startups d'IA verticale utilisent une approche d'ingénierie déployée sur le terrain (forward deployed engineering), héritée de Palantir, pour travailler en étroite collaboration avec les clients afin d'intégrer un contexte spécifique et d'obtenir des évaluations (evals) précises, considérées comme la véritable propriété intellectuelle. Enfin, la discussion révèle les différences de "personnalité" entre les LLM, comme la rigueur de GPT-4 ou la flexibilité de Gemini 2.5 Pro, lorsqu'ils appliquent des rubriques.