Top Spring AI Interview Questions for Freshers and Experienced Developers
Prepare for Spring AI interviews with a hands-on practice experience. Solve curated questions, explore concise explanations, and track your performance with instant scoring.
45 Questions
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1
How do you implement multi-turn conversations?
medium
chat context
Maintain message history.
Key concept: Context persistence.
Store previous interactions.
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2
What is the difference between embeddings and completion models?
medium
embeddings models
Embeddings generate vectors; completion generates text.
Key concept: Different use cases.
Search vs generation.
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3
How do you integrate Spring AI with REST APIs?
easy
api integration
Expose endpoints calling ChatClient.
Key concept: Controller integration.
Example: @RestController.
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4
What is a vector similarity search?
medium
vector search
Finds similar vectors using distance metrics.
Key concept: Cosine similarity.
Used in semantic search.
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5
How do you debug incorrect AI outputs?
hard
debugging llm
Analyze prompts and logs.
Key concept: Iterative refinement.
Check input context.
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6
Explain tool integration in Spring AI.
hard
tools integration
Allows external services in AI workflows.
Key concept: Extend capabilities.
Example: API calls via functions.
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7
What are common performance bottlenecks in Spring AI apps?
hard
performance latency
LLM latency and network overhead.
Key concept: Optimize calls.
Use caching and batching.
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8
How does Spring AI support observability?
medium
monitoring observability
Integrates with logging and metrics.
Key concept: Monitor LLM usage.
Track latency and errors.
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9
What is prompt chaining?
hard
prompt workflow
Link multiple prompts sequentially.
Key concept: Multi-step reasoning.
Output of one feeds next.
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10
How do you manage cost in Spring AI applications?
medium
cost optimization
Reduce tokens and cache responses.
Key concept: Optimize usage.
Monitor API calls.
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11
What is temperature parameter in LLMs?
easy
llm parameters
Controls randomness.
Key concept: Higher = more creative.
Lower = deterministic.
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12
How do you handle rate limits in Spring AI?
hard
rate-limit resilience
Implement retries and backoff.
Key concept: Resilience.
Use Spring Retry.
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13
Explain chunking strategy in RAG systems.
medium
rag chunking
Split large text into smaller parts.
Key concept: Better retrieval.
Example: paragraph chunks.
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14
What is semantic search?
easy
search semantic
Search based on meaning, not keywords.
Key concept: Embeddings.
Example: 'car' matches 'vehicle'.
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15
How do you implement multi-model fallback?
hard
fallback providers
Use fallback logic for providers.
Key concept: Resilience.
Switch on failure.
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16
What is context window in LLMs?
medium
tokens context
Max tokens model can process.
Key concept: Input + output limit.
Impacts prompt size.
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17
How do you prevent prompt injection attacks?
hard
security prompt
Sanitize inputs and validate outputs.
Key concept: Security.
Use strict templates.
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18
What is few-shot prompting?
medium
prompt few-shot
Provide examples in prompt.
Key concept: Guide model behavior.
Improves accuracy.
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19
How do you handle long-running AI tasks?
medium
async processing
Use async processing.
Key concept: Background jobs.
Example: message queues.
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20
What is the difference between completion and chat models?
medium
models chat
Completion is single prompt; chat uses messages.
Key concept: Structured conversation.
Chat is more flexible.
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21
How do you integrate Spring AI with databases?
medium
database integration
Store embeddings and metadata.
Key concept: Hybrid systems.
Example: relational + vector DB.
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22
Explain zero-shot vs few-shot prompting.
medium
prompt learning
Zero-shot has no examples; few-shot includes examples.
Key concept: Context learning.
Few-shot improves results.
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23
How do you validate AI-generated responses?
hard
validation output
Use rules or schema validation.
Key concept: Output control.
Example: JSON schema.
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24
What is guardrails in AI systems?
medium
guardrails safety
Constraints on model output.
Key concept: Safety and compliance.
Example: filter harmful content.
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25
How do you design scalable Spring AI applications?
hard
scaling architecture
Use async, caching, distributed systems.
Key concept: Horizontal scaling.
Example: microservices architecture.
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