LLM Engineering Basics
Building WITH language models rather than just chatting with them — tokens and cost, structured output, injection risk, and why retrieval quality decides everything. The free entry to Applied LLM Engineering.
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What you'll be able to do
- See a language model as a component you call from code, make your first API call, and understand what changes when users are not you.
- Understand tokens as the unit of everything — cost, limits, latency — and design calls that respect the physics.
- Make models return machine-readable JSON reliably, validate it defensively, and assemble a real multi-turn assistant.
- Attack your own assistant, understand why injection has no clean fix, and apply the layered defences and blast-radius thinking real systems use.
- Understand embeddings and retrieval-augmented generation, build a miniature grounded assistant, and learn why retrieval quality decides the system.
- Evaluate LLM systems beyond vibes, see the real Pakistani market for this skill, and know exactly what the paid course builds.
Applied LLM Engineering
Applied LLM Engineering
This free course is the introduction. The programme is taught by a trainer, marked by the academy, and carries the syllabus in full — the part an employer asks about.
See the full syllabus and feeCourse content
- 1 From chatting to building: the API view See a language model as a component you call from code, make your first API call, and understand what changes when users are not you.
- 2 Tokens, context and cost: the physics of the API Understand tokens as the unit of everything — cost, limits, latency — and design calls that respect the physics. Sign in to open
- 3 Structured output and the assistant loop Make models return machine-readable JSON reliably, validate it defensively, and assemble a real multi-turn assistant. Sign in to open
- 4 Prompt injection and the safety duties of builders Attack your own assistant, understand why injection has no clean fix, and apply the layered defences and blast-radius thinking real systems use. Sign in to open
- 5 Grounding: RAG, and measuring retrieval before blaming the model Understand embeddings and retrieval-augmented generation, build a miniature grounded assistant, and learn why retrieval quality decides the system. Sign in to open
- 6 Evaluation, the honest market, and the road to Applied LLM Engineering Evaluate LLM systems beyond vibes, see the real Pakistani market for this skill, and know exactly what the paid course builds. Sign in to open
Course lab
Graded lab A grounded assistant with an eval gate and a cost sheet Open the lab →Who this course is for
- Developers who want to build products on language models, not just chat with them.
- Python learners ready to call an API and handle what comes back.
- Anyone considering the paid Applied LLM Engineering course who wants to try the ground first.
What the 6 lessons cover
- From chatting to building: the API view. You call a model from code so other people get its answers. They did not write the prompt and cannot judge the answer, so their trust becomes your engineering problem.
- Tokens, context and cost: the physics of the API. Cost, limits and speed are all counted in tokens, and Urdu and code use more of them. You work out the monthly bill before building and choose the model size for each task.
- Structured output and the assistant loop. Ask for JSON with an example, use the provider's structured-output mode, and still validate every reply in code, because the model is an unreliable component.
- Prompt injection and the safety duties of builders. Text arriving in the window can pretend to be instructions. There is no clean fix, only layered defences and a small blast radius for anything the model can do.
- Grounding: RAG, and measuring retrieval before blaming the model. Store knowledge outside the model, retrieve the relevant chunks by meaning, and answer only from them. Measure retrieval first, because it decides the system.
- Evaluation, the honest market, and the road to Applied LLM Engineering. Judge systems with a fixed set of real cases run on every change, not with demos, then see the real Pakistani market for the skill and what the paid course adds.
Try the first exercise (in Urdu)
کسی بھی بڑے provider سے free-tier API key لیں، اسے .env میں رکھیں (تصدیق کریں کہ .gitignore اسے چھپاتا ہے)، اور اپنی پہلی scripted call کریں: ایک system message جو ایک محدود assistant متعین کرے (fee desk، library helper — آپ کی مرضی) اور تین مختلف user messages، ہر جواب print کرتے ہوئے۔ پھر رویّہ بدلنے کے لیے صرف system message بدلیں (لہجہ، زبان، انکار) اور دیکھیں: بنانے والے کی طاقت اُس channel میں ہے جو user کبھی نہیں دیکھتا۔
Using this in Pakistan
- AI automation entry roles advertised in September 2026 (Junior AI Automation Engineer, AI Automation Specialist, AI Video Content Creator) are mostly in Lahore; one Lahore junior role offered Rs 50,000 to 80,000 a month. Source
- The data is still thin: on 30 September 2026 Rozee.pk returned about 110 'AI automation' results across Pakistan and 1 in Faisalabad. Source
- The tools these adverts name: n8n, Make, Zapier, OpenAI and webhooks. Source
Questions
Is it really free?
Yes. No payment and no card; lesson 1 needs no account.
Is there a certificate?
Yes, when you complete every lesson.
Can I learn in Urdu?
Every lesson has an Urdu exercise alongside the English reading.
How long does it take?
About 18 minutes of reading, plus the exercises.
What comes next?
The paid Applied LLM Engineering programme, in Faisalabad and live online.
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