Key takeaways
- Handling hallucinations: Integrating fact-checking and cross-validation mechanisms.
- Quality of Service (QoS): Implementing queues, caches, and fallbacks to handle model latency variations.
- Pipelines (ETL/ELT): Designing flows for cleaning, transforming, and versioning data.
- Vector Databases: Mastering vector databases (e.g., Pinecone, Chroma) essential for semantic search.
Towards a Probabilistic World
The architect profession, traditionally based on deterministic logic (if A, then B), is shifting into the probabilistic era.
With AI, system behavior is no longer 100% guaranteed. Architects must design resilient systems capable of handling model unpredictability.
- Handling hallucinations: Integrating fact-checking and cross-validation mechanisms.
- Quality of Service (QoS): Implementing queues, caches, and fallbacks to handle model latency variations.
Data as a Pivot and New Patterns
The software architect is no longer just coding or connecting databases, but orchestrating complex models.
Data at the Forefront
- Pipelines (ETL/ELT): Designing flows for cleaning, transforming, and versioning data.
- Vector Databases: Mastering vector databases (e.g., Pinecone, Chroma) essential for semantic search.
- Governance & Ethics: Ensuring confidentiality and detecting data biases.
New Architectural Tools
- MLOps: Model lifecycle management from training to monitoring.
- RAG (Retrieval-Augmented Generation): Standard for injecting private knowledge into models to reduce errors.
- Agent-Oriented Architecture: Designing systems that don't just respond, but pursue objectives with planning and tools.
AH
Author
AI HUB Editorial
Research Desk
