Title: Llm Application Design — LearnCoachAssist

Q: What is an LLM application?
A: An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow.

Q: What is retrieval-augmented generation (RAG)?
A: RAG retrieves relevant external documents and adds them to model context so answers can use fresh or private knowledge.

Q: Why is grounding important in LLM apps?
A: Grounding ties model output to provided sources, records, or tool results, reducing hallucinations and making answers easier to verify.

Q: What is prompt injection?
A: Prompt injection is an attack where untrusted content tries to override instructions, reveal secrets, or force unintended actions.

Q: What is tool calling?
A: Tool calling lets an LLM request structured actions such as database queries, web searches, calculations, or file edits instead of only producing text.

Q: Why should tools have narrow permissions?
A: Narrow permissions limit damage from mistakes or prompt injection by exposing only the actions and data needed for the task.

Q: What is an evaluation set?
A: An evaluation set is a collection of representative prompts, expected behavior, and scoring rules used to test an LLM app.

Q: What is context window management?
A: Context window management decides what history, documents, tool results, and instructions fit into the model's limited input space.

Q: What is chunking in RAG?
A: Chunking splits documents into smaller passages that can be indexed and retrieved effectively without losing too much meaning.

Q: What is embedding search?
A: Embedding search converts text into vectors and finds semantically similar passages even when exact keywords differ.

Q: What is reranking?
A: Reranking reorders retrieved passages using a stronger relevance model so the most useful context appears first.

Q: What is structured output?
A: Structured output asks the model to return data in a predictable format such as JSON, making it easier to validate and consume.

Q: What does prompt versioning mean in LLM application design?
A: Prompt versioning tracks changes to prompts the same way code changes are tracked.

Q: Why does prompt versioning matter in LLM application design?
A: It makes regressions easier to debug and improvements easier to reproduce.

Q: What is a common mistake with prompt versioning?
A: Editing prompts in production without a history of what changed.

Q: How can you practice prompt versioning?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using prompt versioning?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of prompt versioning?
A: A simple example is using prompt versioning in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does retrieval quality mean in LLM application design?
A: Retrieval quality measures whether the right documents are found for a user question.

Q: Why does retrieval quality matter in LLM application design?
A: Poor retrieval leads to poor answers even when the model is strong.

Q: What is a common mistake with retrieval quality?
A: Blaming the model when the retrieved context was irrelevant.

Q: How can you practice retrieval quality?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using retrieval quality?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of retrieval quality?
A: A simple example is using retrieval quality in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does source citation mean in LLM application design?
A: Source citation links generated claims to supporting documents or records.

Q: Why does source citation matter in LLM application design?
A: It lets users verify answers and increases trust.

Q: What is a common mistake with source citation?
A: Citing sources that were not actually used or do not support the claim.

Q: How can you practice source citation?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using source citation?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of source citation?
A: A simple example is using source citation in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does tool schema mean in LLM application design?
A: A tool schema describes the tool name, inputs, required fields, and allowed values.

Q: Why does tool schema matter in LLM application design?
A: Clear schemas reduce malformed calls and unsafe ambiguity.

Q: What is a common mistake with tool schema?
A: Making tool parameters too broad or poorly described.

Q: How can you practice tool schema?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using tool schema?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of tool schema?
A: A simple example is using tool schema in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does conversation memory mean in LLM application design?
A: Conversation memory stores relevant user or task context across turns or sessions.

Q: Why does conversation memory matter in LLM application design?
A: It enables continuity without forcing users to repeat themselves.

Q: What is a common mistake with conversation memory?
A: Saving sensitive or irrelevant data without a retention policy.

Q: How can you practice conversation memory?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using conversation memory?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of conversation memory?
A: A simple example is using conversation memory in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does guardrail mean in LLM application design?
A: A guardrail is a rule, validation step, or workflow constraint that prevents unsafe or low-quality output.

Q: Why does guardrail matter in LLM application design?
A: It adds reliability around probabilistic model behavior.

Q: What is a common mistake with guardrail?
A: Treating guardrails as a substitute for testing and monitoring.

Q: How can you practice guardrail?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using guardrail?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of guardrail?
A: A simple example is using guardrail in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does model routing mean in LLM application design?
A: Model routing sends tasks to different models based on cost, latency, difficulty, or capability.

Q: Why does model routing matter in LLM application design?
A: It improves efficiency and user experience.

Q: What is a common mistake with model routing?
A: Sending every task to the largest model by default.

Q: How can you practice model routing?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using model routing?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of model routing?
A: A simple example is using model routing in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does context compression mean in LLM application design?
A: Context compression summarizes or filters older information to fit within the model context window.

Q: Why does context compression matter in LLM application design?
A: It preserves useful continuity while controlling cost and latency.

Q: What is a common mistake with context compression?
A: Compressing away details needed for accurate decisions.

Q: How can you practice context compression?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using context compression?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of context compression?
A: A simple example is using context compression in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does fallback response mean in LLM application design?
A: A fallback response is what the app says or does when the model or tool fails.

Q: Why does fallback response matter in LLM application design?
A: It keeps the product graceful under outages, rate limits, or uncertainty.

Q: What is a common mistake with fallback response?
A: Showing raw errors to users without guidance.

Q: How can you practice fallback response?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using fallback response?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of fallback response?
A: A simple example is using fallback response in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does red teaming mean in LLM application design?
A: Red teaming deliberately tests an LLM app with adversarial or unusual inputs.

Q: Why does red teaming matter in LLM application design?
A: It reveals vulnerabilities before users or attackers find them.

Q: What is a common mistake with red teaming?
A: Only testing happy-path prompts.

Q: How can you practice red teaming?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using red teaming?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of red teaming?
A: A simple example is using red teaming in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does PII handling mean in LLM application design?
A: PII handling defines how personal data is collected, masked, stored, and sent to models.

Q: Why does PII handling matter in LLM application design?
A: It protects users and reduces compliance risk.

Q: What is a common mistake with PII handling?
A: Sending sensitive data to tools or logs unnecessarily.

Q: How can you practice PII handling?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using PII handling?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of PII handling?
A: A simple example is using pii handling in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does human escalation mean in LLM application design?
A: Human escalation routes uncertain or high-impact cases to a person.

Q: Why does human escalation matter in LLM application design?
A: It prevents automation from making risky decisions alone.

Q: What is a common mistake with human escalation?
A: Letting the model act autonomously in sensitive workflows.

Q: How can you practice human escalation?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using human escalation?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of human escalation?
A: A simple example is using human escalation in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does output parser mean in LLM application design?
A: An output parser validates and converts model text into structured application data.

Q: Why does output parser matter in LLM application design?
A: It makes model output usable by software systems.

Q: What is a common mistake with output parser?
A: Assuming the model will always return perfectly formatted JSON.

Q: How can you practice output parser?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using output parser?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of output parser?
A: A simple example is using output parser in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does prompt caching mean in LLM application design?
A: Prompt caching reuses repeated context or instructions to reduce cost and latency.

Q: Why does prompt caching matter in LLM application design?
A: It helps apps with large stable context blocks.

Q: What is a common mistake with prompt caching?
A: Caching dynamic or user-specific data incorrectly.

Q: How can you practice prompt caching?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.

Q: What question should you ask when using prompt caching?
A: Ask: what problem is this solving, what trade-off does it create, and how will I know it worked?

Q: What is a simple example of prompt caching?
A: A simple example is using prompt caching in a small project, note, or decision before applying it to a larger real-world situation.

Q: What does observability mean in LLM application design?
A: Observability tracks requests, model choices, tools, latency, cost, and failures.

Q: Why does observability matter in LLM application design?
A: It makes production LLM behavior diagnosable.

Q: What is a common mistake with observability?
A: Launching without logs that connect user problems to model traces.

Q: How can you practice observability?
A: Apply it to a small realistic example, explain the decision out loud, then check whether the result matches the intended outcome.