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LLM Development in Practice

Advanced70 min13 chaptersFree

Course Contents

  1. 31.1 LLM Development Overview
  2. 31.2 API Basics: Your First Call
  3. 31.3 Prompting in Code
  4. 31.4 RAG: Retrieval-Augmented Generation
  5. 31.5 Vector Databases
  6. 31.6 Fine-Tuning: When and Why
  7. 31.7 Development Frameworks
  8. 31.8 Build: A Working Example
  9. 31.9 Safety in Development
  10. 31.10 Deployment
  11. 31.11 Monitoring and Optimisation
  12. 31.12 Enterprise Case Studies
  13. 31.13 Summary

31.1 LLM Development Overview

Building with LLMs follows a maturity ladder: prompt engineering → retrieval (RAG) → fine-tuning. Each rung costs roughly 10x more in time and data than the last. The professional path is to exhaust each rung before climbing: most "we need fine-tuning" requests are actually retrieval or prompting problems in disguise.

31.2 API Basics: Your First Call

  1. Get an API key from any major provider; $5 of credit goes a long way for learning.
  2. Make a request: POST your prompt, receive structured JSON with the response.
  3. Learn three parameters: model (which engine), temperature (0 = deterministic, 1 = creative), max_tokens (output length cap).
  4. Read the usage field: token consumption is your bill — this is how you forecast costs.
Minimal test call (Python) from openai import OpenAI client = OpenAI(api_key="YOUR_KEY") resp = client.chat.completions.create( model="gpt-4o-mini", temperature=0.3, messages=[{"role":"user","content":"Explain tokens in one sentence"}]) print(resp.choices[0].message.content) print(resp.usage) # your token bill
Once these eight lines run, you can automate any text workflow — that is the real milestone.

31.3 Prompting in Code

Every prompt-engineering technique applies directly in code — plus two new ones: system messages and structured outputs. System messages set persistent behaviour across a session; requesting JSON output with a fixed schema turns the model into a reliable component other software can consume. Version-control your prompts like code: change one variable at a time and keep a test set.

31.4 RAG: Retrieval-Augmented Generation

RAG = open-book exam for AI. Instead of hoping the model memorised your documents during training, you store them in a searchable index, retrieve the relevant passages per question, and hand them to the model as context. Benefits: answers grounded in your real documents, citations possible, knowledge updatable in minutes — no retraining.

Business Example

A pet-supplies wholesaler indexed 3,000 SKUs of product docs, quote history and logistics FAQs. Customer-service staff were asked daily: "When will this cat food restock?" "What's the MOQ?" With RAG, the system retrieves the right passages and generates grounded answers. Hallucination rates fell sharply; new-staff onboarding dropped from two weeks to three days.

31.5 Vector Databases

A vector database stores text as embeddings — numeric vectors capturing meaning — so "similar meaning" is findable by distance, not keywords. It is the index that makes RAG's retrieval step fast and semantically aware: "why is my order late" finds the shipping-delay section even though no word overlaps. Managed options (Pinecone, cloud-native services) remove most operational work for SMB-scale projects.

31.6 Fine-Tuning: When and Why

Common mistakeFine-tuning to inject knowledge
Right approachThat's RAG's job — fine-tuning changes style/behaviour, not facts
Common mistakeFine-tuning before 100+ quality examples exist
Right approachCollect real interactions first; most needs dissolve with better prompts

31.7 Development Frameworks

Frameworks (LangChain, LlamaIndex and friends) package the common patterns: document loaders, retrieval chains, agent loops. For prototypes they accelerate; for production, evaluate honestly whether a thin custom layer is simpler — every abstraction has failure modes you'll eventually debug. The no-code alternative (Zapier + APIs) covers many SMB automation needs without code.

31.8 Build: A Working Example

  1. Pick one repetitive text task with clear input/output (e.g. summarising customer emails into tickets).
  2. Collect 30 real examples: input + what the ideal output looks like.
  3. Write the prompt with few-shot examples; test against all 30, score accuracy.
  4. Add a fallback: confidence low → route to human.
  5. Automate the plumbing: schedule, email integration, logging.

31.9 Safety in Development

31.10 Deployment

Start inside your team, not with customers. Internal deployment surfaces failure modes cheaply; customer-facing rollouts deserve a staged percentage ramp with monitoring. Keep prompts and schemas in version control so any regression can be rolled back in minutes.

31.11 Monitoring and Optimisation

31.12 Enterprise Case Studies

Three patterns that work
Common mistakeStarting with fine-tuning
Right approachPrompt + RAG gets 80%; tune only if style/format must change
Common mistakeNo evaluation baseline
Right approach30-50 real test cases before launch; re-run after every change
Common mistakeNo fallback path
Right approachLow-confidence → human handoff is mandatory, not optional

31.13 Summary

Summary

You know the ladder (prompt → RAG → tune), your first API call, the RAG architecture and its vector index, safety guardrails, and the deployment path. Security details next: AI Security in Practice →

Keep learning

Advanced tools track
Beyond

Frequently Asked Questions

Do I need to code to learn LLM development?
The API and RAG chapters need only basic concepts — sample code is copy-paste ready. Zero-code paths (Zapier + no-code tools) are covered in the workflow-automation course.
RAG or fine-tuning — which first?
RAG covers nine in ten scenarios: knowledge updates often, needs citations, budget-conscious. Fine-tune only to change style or output format, not to inject facts.

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