Membangun dengan Sistem AI

• Choose an architecture based on task and evidence needs.
• Evaluate quality, latency, cost, and safety together.
• Design monitoring and incident response before launch.

Recommended first: Yayasan AI, Pengguna AI yang bertanggung jawab

1. 1

   Language-model systems

   Outcome: Reason about tokens, context, generation, and model tradeoffs.

   Practice: Compare two model options using quality, latency, context, privacy, and cost.

   Language-model mechanicsModels, inference, and limitationsModel cost and operational tradeoffs
   ChatGPT & LLM →Model AI Dijelaskan →Inferensi AI →
2. 2

   Grounding with retrieval

   Outcome: Know when and how retrieval can improve evidence access.

   Practice: Design a retrieval test set with answer and citation requirements.

   Generasi yang ditambah pengambilanSource and claim verification
   Kualitas Pengambilan →Basis Data Vektor →Penyematan →
3. 3

   Agents and tools

   Outcome: Bound multi-step systems with permissions and checkpoints.

   Practice: Write an agent permission model and failure-recovery path.

   Agents, tools, and long-running tasksAutomation boundaries and safeguardsAI security and misuse risk
   Agen AI →Otomatisasi Alur Kerja AI →
4. 4

   Evaluation and operations

   Outcome: Measure system performance before and after deployment.

   Practice: Create an evaluation suite covering quality, refusal, latency, cost, and regressions.

   Evaluation and benchmark literacySuccess metrics and monitoringAI incident responseExperiment and pilot design
   Tolok Ukur AI →Keamanan AI →