Real-World LLM Scenarios

Before you continue: three tools for this module

• Token: a piece of text processed by the model.
• Parameter: a learned number controlling the model’s transformations.
• Inference: using the trained model without updating its parameters.

You do not need to memorize these yet. Use this map when the terms reappear.

Begin with the central question

What hidden problem does Real-World LLM Scenarios solve inside a real language-model system?

Keep that central question about Real-World LLM Scenarios in mind. The definitions, numbers, diagrams, and code examples below answer it one piece at a time.

business problem → constraints → architecture choice → evaluation → monitoring

1. What You Will Learn

Learning outcomes

• Translate business requirements into an appropriate LLM system design.
• Choose among prompting, RAG, fine-tuning, tools, and traditional software.
• Identify failure modes and evaluation plans before deployment.
• Reason explicitly about privacy, reliability, latency, and cost tradeoffs.

In one sentence

💡 Big picture

Real projects begin with a problem and constraints, then choose the simplest combination of models, data, tools, and software that can solve it.

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2. Why This Module Exists

The problem this module solves

• Knowing isolated concepts does not automatically teach architecture decisions.
• Scenarios help you practise tradeoffs involving quality, safety, privacy, latency, cost, and maintenance.

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Scenario 1 — Build a Customer Support Chatbot

Problem: Handle common customer questions, escalate complex issues.

Architecture: LLM (instruction-tuned, Module 17) + RAG (Module 20, 27) for product/policy knowledge + possibly a classifier (Module 22’s task-routing pattern) for escalation decisions.

Concept involved: Module 20’s decision framework — RAG for frequently-updated policy/product info; fine-tuning (Module 16) for consistent support tone.

Why appropriate: Support content changes regularly (favors RAG over fine-tuning for facts); consistent tone benefits from fine-tuning or careful prompting.

Alternatives: Pure prompting with policy docs included directly, if the knowledge base is small enough to fit context (Module 3).

Trade-offs: RAG adds retrieval latency and infrastructure complexity; pure prompting doesn’t scale to large knowledge bases.

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Scenario 2 — Build a Document Q&A System

Problem: Answer questions about a large set of documents.

Architecture: RAG (Module 20, 27) — embed documents, retrieve relevant chunks per query, generate grounded answers.

Concept involved: Module 3’s context window limits (documents too large to fit directly) directly motivate RAG’s chunked retrieval approach.

Why appropriate: Document collections typically exceed context window capacity; RAG retrieves only what’s relevant per query.

Alternatives: Long-context models (Module 3) if the document set is small enough to fit entirely, avoiding retrieval complexity.

Trade-offs: RAG’s retrieval quality directly bounds answer quality (Module 21’s grounding limitation); long-context models cost more per query (Module 14’s quadratic scaling) but avoid retrieval engineering.

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Scenario 3 — Build a Coding Assistant

Problem: Help developers write, review, and debug code.

Architecture: A capable general-purpose or code-specialized LLM, often with low temperature (Module 15) for precision, potentially with RAG over a specific codebase for context-aware suggestions.

Concept involved: Module 15’s temperature guidance — code generation benefits from low temperature/near-greedy decoding for consistency and syntactic correctness.

Why appropriate: Code has strict correctness requirements; high randomness (Module 15’s demonstrated entropy increase) raises the risk of syntactically or logically invalid output.

Alternatives: Fine-tuning (Module 16) on a specific codebase/style for deeper customization beyond what prompting/RAG alone achieve.

Trade-offs: Lower temperature reduces creative variation but improves reliability — the right trade-off for most coding contexts.

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Scenario 4 — Build an Internal Company Knowledge Assistant

Problem: Answer employee questions using internal, proprietary documentation.

Architecture: RAG (Module 20) over internal document stores, likely self-hosted (Module 25) given data privacy considerations for proprietary content.

Concept involved: Module 25’s privacy trade-off directly applies — proprietary internal data may favor self-hosting over sending queries to a third-party API.

Why appropriate: Internal knowledge is proprietary, frequently updated, and often too large for direct context inclusion — a natural fit for RAG’s retrieval-at-query-time approach.

Alternatives: API-based models if data sensitivity is lower and rapid prototyping matters more (Module 25’s trade-off table).

Trade-offs: Self-hosting adds infrastructure burden (Module 14, 24) but addresses privacy concerns directly.

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Scenario 5 — Build a Summarization System

Problem: Condense long documents or conversations into concise summaries.

Architecture: LLM generation task (a core, direct LLM capability — Module 7’s generation loop), potentially combined with chunking strategies for documents exceeding context limits (Module 3).

Concept involved: Module 23’s evaluation challenge directly applies — summarization is open-ended, so exact-match-style evaluation is inappropriate; BLEU/ROUGE-style or human/LLM-judge evaluation fits better.

Why appropriate: Summarization is a natural generation task LLMs handle well directly, without necessarily requiring RAG or fine-tuning.

Alternatives: Fine-tuning (Module 16) for a very specific summary style/format requirement.

Trade-offs: Very long documents require careful chunking strategy (Module 3) to avoid losing important context across chunks.

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Scenario 6 — Build a Structured Data Extraction System

Problem: Extract specific structured fields (names, dates, amounts) from unstructured text.

Architecture: LLM with careful prompting for structured output format, low temperature (Module 15) for consistency, possibly fine-tuned (Module 16) for a very specific, high-volume extraction schema.

Concept involved: Module 15’s temperature guidance and Module 23’s evaluation — exact match may actually be appropriate HERE (unlike Scenario 5), since extracted fields often do have a single correct value.

Why appropriate: Structured extraction benefits from low-randomness, consistent output; exact match evaluation fits since correct values are typically unambiguous.

Alternatives: Traditional NLP techniques (NER, your NLP course) for simpler, well-defined extraction tasks that don’t need full LLM capability.

Trade-offs: LLM-based extraction offers more flexibility for varied input formats but costs more per request than a lightweight classical NLP pipeline (NLP course Module 6’s cost/capability trade-off, mirrored here).

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Scenario 7 — Build a Domain-Specific Assistant (Medical, Legal)

Problem: Provide accurate, domain-appropriate assistance in a high-stakes field.

Architecture: RAG over verified domain sources (Module 20) + possibly fine-tuning (Module 16) for domain terminology/style + mandatory human review for high-stakes outputs.

Concept involved: Module 21’s hallucination risk is especially consequential here — grounding via RAG and active verification are essential, not optional, given the stakes.

Why appropriate: High-stakes domains require maximal grounding and verification, given hallucination’s structural, unavoidable possibility (Module 21).

Alternatives: None fully substitute for human review in genuinely high-stakes applications — LLM assistance should augment, not replace, qualified human judgment in these domains.

Trade-offs: Extensive grounding/verification adds cost and latency, but is a necessary trade-off given the stakes.

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Scenario 8 — Build a High-Throughput LLM API

Problem: Serve LLM responses to a very large number of concurrent users cost-effectively.

Architecture: Quantized models (Module 24), continuous batching (Module 24), KV caching (Module 14) — a full serving optimization stack.

Concept involved: Module 24’s complete optimization toolkit directly applies — quantization for memory efficiency, continuous batching for GPU utilization, KV caching as foundational infrastructure.

Why appropriate: High throughput requirements directly demand every relevant optimization from Module 14/24, verified directly to produce substantial (8x memory, avoiding significant utilization waste) improvements.

Alternatives: Distillation (Module 24) to a smaller model if the capability trade-off is acceptable for the specific task volume.

Trade-offs: Aggressive optimization can trade some accuracy/latency consistency for substantially reduced cost — a genuine, evaluatable choice per application.

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Scenario 9 — Reduce LLM Inference Cost

Problem: An existing LLM-powered application’s operating costs are too high.

Architecture: Apply Module 24’s techniques systematically: quantization, continuous batching, and evaluate whether a smaller/ distilled model suffices for the actual task complexity (Module 12-13’s capacity-vs-need evaluation).

Concept involved: Module 12’s “more parameters ≠ automatically better” and Module 20’s decision framework — many tasks don’t need the largest available model.

Why appropriate: Cost reduction should target genuine inefficiencies (verified directly in Module 24: static batching waste, unnecessary precision) rather than uniformly reducing capability everywhere.

Alternatives: Caching frequent/repeated queries; reducing unnecessary context (Module 2-3’s token cost) sent per request.

Trade-offs: Each optimization has some capability/consistency cost — the right combination depends on the specific application’s actual accuracy requirements.

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Scenario 10 — Reduce Hallucinations

Problem: An LLM-powered application occasionally produces confidently incorrect information.

Architecture: RAG grounding (Module 20, 21), output verification against trusted sources, careful prompting (asking the model to indicate uncertainty or cite sources), and possibly lower temperature (Module 15) for more conservative generation.

Concept involved: Module 21’s complete hallucination mechanism — the fix must address grounding, since hallucination isn’t a bug to be patched but a structural property of the underlying mechanism.

Why appropriate: Directly targets Module 21’s demonstrated root cause — providing genuine grounding (RAG) and reducing reliance on purely parametric (trained-in) knowledge for facts that matter.

Alternatives: Human review workflows for especially high-stakes outputs, where automated mitigation alone isn’t sufficient.

Trade-offs: No mitigation fully eliminates hallucination risk (Module 21’s explicit limitation) — the goal is substantial reduction and appropriate handling, not a guaranteed elimination.

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What You Should Remember

• Every scenario maps to specific, already-covered concepts — none of these problems require new mechanisms beyond what this course has built.
• Trade-offs are genuine and evaluatable in every scenario — there’s rarely a single “correct” architecture, only better and worse fits for specific requirements.
• Real engineering reasoning combines multiple modules’ concepts together — Module 20’s decision framework, Module 21’s hallucination mechanism, and Module 24’s optimization toolkit recur across nearly every scenario.

Next Step

Next: Module 29 — Interview Questions — realistic, in-depth questions spanning this entire course, plus “LLMs — What You Should Know Now,” an “LLM Cheat Sheet,” and a final consolidated interview question bank.