FORMATION IA
Ingénierie des Prompts Avancée pour Ingénieurs Logiciels
Maîtrisez les techniques de prompting avancées pour concevoir des applications LLM fiables, testables et scalables en production.
Ce qu'elle couvre
Ce programme de niveau praticien dote les ingénieurs de stratégies avancées de prompting utilisées dans les systèmes LLM en production : génération de sorties structurées, function calling, raisonnement par chaîne de pensée et conception de patterns few-shot. Les participants apprennent à construire des boucles d'itération pilotées par l'évaluation pour mesurer et améliorer les performances des prompts de manière systématique. Le format combine des ateliers de codage pratiques avec des études de cas réels, couvrant les API OpenAI et les modèles open source. À l'issue de la formation, les ingénieurs sont capables de concevoir, tester et maintenir des pipelines de prompts robustes prêts pour la production.
À l'issue, vous saurez
- Design and validate structured-output prompts that reliably return well-formed JSON conforming to a defined schema
- Implement function-calling pipelines in which an LLM correctly selects and parameterises tools across multi-turn conversations
- Apply chain-of-thought and self-consistency techniques to measurably improve model accuracy on reasoning tasks
- Build an automated prompt evaluation suite with quantitative metrics and integrate it into a CI pipeline
- Version and regression-test prompt templates so that model upgrades do not silently degrade production behaviour
Sujets abordés
- Structured output generation: JSON mode, grammar-constrained decoding, and schema validation
- Function calling and tool use: designing reliable tool-calling agents
- Chain-of-thought and reasoning prompts: zero-shot CoT, self-consistency, tree-of-thought
- Few-shot and many-shot prompting: pattern selection, example ordering, and diversity
- Eval-driven prompt iteration: building automated test suites for prompt quality
- System prompt architecture: role separation, context management, and injection defence
- Retrieval-augmented generation (RAG) prompt integration and grounding strategies
- Prompt versioning, regression testing, and CI/CD integration for LLM pipelines
Modalité
Delivered as a 2-3 day intensive bootcamp, available in-person or fully remote via video conferencing with shared coding environments (e.g., GitHub Codespaces or JupyterHub). Approximately 70% of time is hands-on lab work; 30% is concept delivery and code review. Participants work on a capstone prompt pipeline project throughout. Materials include a private GitHub repo with starter notebooks, evaluation harness templates, and a reference prompt library. A follow-up 90-minute Q&A session is included two weeks after the bootcamp.
Ce qui fait que ça marche
- Establishing an eval harness with clear metrics before starting prompt iteration, not after
- Treating prompt engineering as a collaborative discipline between product, data, and engineering teams
- Running prompt regression tests in CI so every model or prompt change is automatically validated
- Starting with the simplest effective prompt and adding complexity only when evals demonstrate a need
Erreurs fréquentes
- Treating prompts as static strings rather than versioned, testable artefacts managed in source control
- Relying on manual human review instead of automated evals, making prompt iteration slow and subjective
- Ignoring model-specific behaviours and assuming prompts transfer perfectly across different LLMs or model versions
- Over-engineering complex chained prompts before validating that simpler approaches fail on the actual task
Quand NE PAS suivre cette formation
This training is not the right fit for a team that has not yet shipped any LLM feature and is still evaluating whether to use AI at all — start with a literacy or awareness workshop first to align on use cases before investing in advanced prompting techniques.
Fournisseurs à considérer
Sources
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