AI Plugin Developer Interview Questions

A great answer covers LLM API integration, conversational or tool-use interfaces, and the non-deterministic nature of AI-powered outputs versus rule-based traditional plugins.

Covers tool/function definitions in the API request, the model returning a function_call object with JSON arguments, and the developer's responsibility to execute and return results.

Discovers the structured declaration file (e.g., GPT Actions schema, OpenAPI spec) that tells the host platform what the plugin does, its endpoints, authentication requirements, and how the LLM should invoke it.

Covers exponential backoff, request queuing, caching strategies, and graceful degradation to simpler models or cached responses.

Explains tokenization basics, context window limits, cost implications of token usage, and strategies for summarization and truncation to stay within budgets.

Covers NL-to-SQL generation, schema introspection, safety constraints (read-only, parameterized queries), result formatting, and error handling for ambiguous queries.

Covers authorization code flow, redirect URIs, token storage and refresh, scope management, and the plugin manifest's auth configuration.

Covers RAG grounding, structured output constraints, confidence scoring, citation requirements, temperature tuning, and output validation with downstream checks.

Discusses golden test sets, snapshot testing with temperature=0, semantic similarity evaluation, tool-call correctness metrics, and human-in-the-loop evaluation.

Covers manifest format differences, authentication approaches, supported action types, distribution channels, and ecosystem maturity.

Covers conversation state management, context window budgeting, sliding window summarization, and designing tool descriptions that work well with accumulated context.

Covers semantic understanding vs. exact match, latency differences, index maintenance, hybrid approaches, and when each is appropriate.

Covers clear naming, concise but specific descriptions, parameter documentation, examples in descriptions, and avoiding overlap between tool capabilities.

Covers blue-green deployments for prompts, schema backward compatibility, canary rollouts, feature flags for prompt variants, and user communication.

Covers token counting per request, input vs. output token pricing, per-endpoint cost tracking, alerting on cost anomalies, and strategies like caching and model tiering.

Covers provider abstraction layers, capability-based routing, health checks, latency-based failover, cost-aware scheduling, and unified tool-calling format translation.

Covers ReAct or plan-and-execute agent patterns, tool dependency graphs, checkpoint/rollback mechanisms, human-in-the-loop gates, and timeout management.

Covers input sanitization, sandboxed execution environments, allowlisted operations, output validation, prompt injection detection, and principle of least privilege for tool permissions.

Covers automated eval harnesses, LLM-as-judge evaluation, user feedback loops, A/B testing infrastructure, safety classifiers, and regression detection across deployments.

Covers sandboxing policies, automated safety reviews, capability-based permission systems, quality scoring, versioning standards, and revenue-sharing models.

Covers hierarchical memory (working vs. long-term), progressive summarization, priority-based context eviction, external scratchpad storage, and checkpointing state.

Covers OpenAI's streaming with function calls, server-sent events, partial JSON parsing, tool execution during stream pause, and seamless resumption of generation.

Covers data minimization, PII redaction pipelines, audit logging, data residency requirements, opt-in consent flows, and evaluating whether to use on-prem or API-based LLMs.

Covers benchmark datasets, confusion matrices for tool selection, argument schema validation, few-shot examples in tool descriptions, fine-tuning for tool use, and regression testing.

Covers feedback collection, dynamic few-shot example selection, retrieval-augmented prompt construction, user preference profiles, and continuous evaluation loops.

Covers checking if the LLM is hallucinating URLs vs. receiving stale data, implementing URL validation before returning responses, adding RAG grounding from a live product catalog, and adding disclaimer language.

Covers distributed tracing, isolating whether the bottleneck is in LLM call latency, tool execution, serialization, or the host platform, and implementing latency budgets per component.

Covers RAG from a verified legal database, citation verification against external APIs, confidence scoring, mandatory source URLs, and clear disclaimers about AI limitations.

Covers multi-provider failover architecture, cached response serving, graceful degradation to a simpler model, user communication strategy, and post-incident review.

Covers feature flags, canary deployment to 5% of users, monitoring tool selection accuracy and error rates, rollback triggers, and schema backward compatibility.

Covers input pattern detection, system prompt hardening, tool permission boundaries, output validation against expected schemas, and behavioral monitoring for anomalous tool usage patterns.

Covers prompt compression, switching to cheaper models for simple tasks, aggressive caching, batching similar requests, optimizing tool descriptions to reduce unnecessary calls, and implementing tiered model routing.

Covers vision API integration, image preprocessing and resizing for token efficiency, combining image analysis with product catalog retrieval, and fallback for unsupported image types.

Covers multi-tenant architecture, SSO/SAML integration, usage metering and billing, SLA commitments, data isolation, and enterprise security review processes.

Covers multilingual prompt engineering, testing with native speakers, handling character encoding in tool inputs/outputs, locale-aware formatting, and evaluating model multilingual performance.

Covers defining Tool objects, initializing an AgentExecutor with a ReAct or OpenAI Functions agent, memory management, and handling the agent's intermediate reasoning steps.

Covers SimpleDirectoryReader, VectorStoreIndex construction, query engine configuration with similarity_top_k, response synthesizers, and integrating the index as an API endpoint.

Covers thread creation, assistant configuration with tools, file upload, message handling, run polling, and extracting structured results from the assistant's responses.

Covers useChat/useCompletion hooks, server-side streaming with OpenAIStream, ai/rsc for React Server Components, and handling tool-call streaming with onData callbacks.

Covers using the huggingface_hub client, model selection on the HF Hub, handling model loading delays (cold starts), combining specialized model outputs with LLM reasoning, and fallback strategies.

Covers instrumenting chains with tracing, capturing input/output at each step, filtering traces by user or session, evaluating against test datasets, and using the playground for prompt iteration.

Covers Bedrock's InvokeModel API, model ID configuration, guardrails setup, cross-model prompt format differences, and building a router that maps task types to optimal models.

Covers Workers AI binding in wrangler.toml, model selection for edge deployment, handling cold starts, combining with D1/KV for context storage, and deploying to the edge network.

Covers defining Pydantic BaseModel schemas, converting them to OpenAI function definitions, using Instructor library for validated extraction, and handling validation errors gracefully.

Covers creating eval datasets with expected outputs, running prompt variants against the dataset, scoring with rubric-based LLM judges, comparing metrics (accuracy, latency, cost), and versioning prompts in source control.

Look for a structured decision-making process involving data (cost metrics, user value), stakeholder alignment, experimentation, and a clear outcome with measurable results.

Look for incident response skills, root cause analysis, user communication, technical remediation, and proactive measures taken to prevent recurrence.

Look for structured learning habits (newsletters, communities, hands-on experimentation), and evidence of translating new knowledge into practical improvements.

Look for empathy, clear communication without jargon, offering alternative solutions, and managing expectations while maintaining trust.

Look for user segmentation thinking, data-driven prioritization, A/B testing approaches, and balancing broad utility with edge-case handling.