Prompt & Version Management
Prompts as reviewed, versioned, evaluated production configuration.
4 to work through
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intermediate
A platform's prompts are embedded in application code and changed frequently. What problems arise, and how should prompts be managed?
2 min answer -
intermediate
An engineer wants to change the system prompt of a live customer-facing assistant at 16:00 on a Friday. Tell me what has to already be true for that to be a routine change and what you would ask before approving it.
3 min answer -
intermediate
Answer quality degraded overnight. No code was deployed. What are the candidate causes?
2 min answer -
advanced
140 prompts live as string literals across nine services. You must move them into a central prompt registry with no quality regression and no big-bang cutover, while every team keeps shipping. What is the sequence, and where can it go wrong?
3 min answer
2 terms in this topic
Prompt Regression Suite
A set of test cases with expected properties, run against a prompt on every change, to detect quality regressions before deployment.
practicePrompt Versioning
Treating prompts as versioned, reviewed, tested and deployable artifacts rather than as strings edited in place.
Neighbouring topics
AI-Era Architecture
General material on architecting systems that include models.
LLM Application Architecture
The shape of a production system with a model in the request path.
RAG Architecture
Retrieval, grounding, citation and the permissions RAG can enforce.
Vector Databases
Approximate nearest-neighbour search, filtering and re-indexing.
Embeddings
Dense representations, model coupling and the migration they imply.
Chunking & Retrieval
Structure-aware splitting, hybrid search and why chunking dominates quality.
Reranking
Cross-encoders improving precision more than a bigger embedding model.
Model Selection
Capability, latency, cost and the evaluation that decides between them.
AI Gateways
Centralised routing, keys, quotas, caching, logging and safety policy.
Agent Architectures
Loops, planning, memory and the boundaries an agent must not cross.
Tool Calling
Typed tool interfaces, narrow parameters and per-tool authorisation.
Multi-Agent Systems
Coordination, hand-off and whether more agents actually help.
LLM Evaluation
Held-out sets, rubric judging, CI gates and production sampling.
AI Observability
Logging prompts, versions, retrieved context and cost per request.
Guardrails
Deterministic checks on input and output that fail closed.
Prompt Injection Defence
Breaking the private-data, untrusted-input, outbound-channel combination.
AI Cost Management
Token accounting, routing, caching and the context-window budget.
Human in the Loop
Gating by reversibility and blast radius, and avoiding approval fatigue.
ML Platform
Feature stores, training pipelines, registries and deployment.