Evaluation Production And Capstone
Where to Go Next
This is a short, reflective closing chapter — not new material. Its job is placing this NLP & LLM curriculum in context: what it covered, how it connects to eve
Jr Codex NLP & LLM Notes
Level: Advanced Prerequisites: Chapter 4: Capstone — End-to-End RAG Application Time to complete: ~15 minutes
Table of Contents
- Purpose of This Chapter
- What This Curriculum Covered
- How This Curriculum Fits the Larger Jr Codex Path
- Staying Current in a Fast-Moving Field
- Directions for Continued Depth
- A Final Word
1. Purpose of This Chapter
This is a short, reflective closing chapter — not new material. Its job is placing this NLP & LLM curriculum in context: what it covered, how it connects to everything that came before it, and how to keep learning in a field that moves unusually fast.
2. What This Curriculum Covered
The Nine Modules, In Review
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1. Foundations — what NLP is, classical
toolkit, historical arc
2. Word Embeddings — one-hot to dense to
contextual representations
3. Tokenization & — bridging the DL Notes'
Pre-training Transformer to language,
Foundations MLM/CLM objectives
4. Encoder Models — BERT, its
(BERT) pretraining, fine-
tuning, and family
5. Decoder Models — GPT, scaling
(GPT) laws, in-context
learning, the
modern landscape
6. Fine-Tuning & — instruction
Aligning LLMs tuning, LoRA/
QLoRA, RLHF/
DPO
7. Prompt Engineering — training-
free
techniques,
CoT,
structured
output
8. RAG & Agents — grounding
in external
knowledge,
multi-step
action,
multi-agent
systems
9. Evaluation, Production — measuring
& Capstone quality,
deploying
responsibly,
end to end
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3. How This Curriculum Fits the Larger Jr Codex Path
The Full Arc, From the Beginning
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Python Notes: programming fundamentals
↓
Data Science Notes: statistics, data wrangling,
experimentation
↓
Machine Learning Notes: the complete classical ML
toolkit — supervised,
unsupervised, reinforcement
↓
Artificial Intelligence classical/symbolic AI —
Notes: search, logic, planning,
agents, RL foundations
↓
Deep Learning Notes: neural networks,
CNNs, RNNs,
Transformers,
generative models
↓
NLP & LLM Notes (THIS language-specific
curriculum): application of
the Transformer
— YOU ARE HERE
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Every technique in this curriculum built on something from an earlier curriculum, rather than existing in isolation — Word2Vec's self-supervised training echoed autoencoders; BERT's fine-tuning was a direct instance of transfer learning; RLHF was a direct application of the ML Notes' reinforcement learning chapter; agents extended the AI Notes' classical agent framework with an LLM-based decision-maker. This is the payoff of following the curricula in order: nothing here needed to be learned "cold."
4. Staying Current in a Fast-Moving Field
Module 5, Chapter 4 deliberately avoided naming specific, fast-changing models — a genuine, practical acknowledgment that this field moves faster than any static curriculum can track model-by-model.
What Stays Stable vs What Changes Quickly
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STABLE (this curriculum's self-attention (DL Notes,
actual focus): Module 6), the encoder/
decoder-only
distinction (Module 3-5),
pre-training objectives
(Module 3), RAG's core
architecture (Module 8),
evaluation principles
(Module 9)
CHANGES QUICKLY (worth specific model names
tracking separately, and benchmark
ongoing): leaderboards,
exact API
pricing/features,
newest prompting/
agent frameworks
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The stable foundation covered across these nine modules is what makes newly-released models and techniques quickly understandable — a new model is, almost always, a variation on architecture and training concepts already covered here, not something requiring an entirely new mental model.
5. Directions for Continued Depth
If You Want to Go Deeper Still
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Research papers directly: the ORIGINAL papers behind
BERT, GPT, RLHF, and RAG
(Modules 4-8) are now
readable, given this
curriculum's foundation —
reading them directly
builds deeper, more precise
understanding than any
secondary summary
Building REAL projects: the capstone (Chapter 4) is
a STARTING template — a
genuinely useful next
step is building a
SIMILAR system for a
REAL, personally
motivating use case
Specializing further: multimodal models
(Module 5, Ch.4,
Sec.5), efficient
inference/serving at
scale (Module 9, Ch.3),
or alignment research
(Module 6, Ch.3) are
all deep, active areas
this curriculum only
introduced
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6. A Final Word
You've now completed the entire Jr Codex technical curriculum — from Python fundamentals through the architecture and application of modern large language models. Every module built on the ones before it, and the foundational concepts (gradient descent, attention, transfer learning, evaluation discipline) recur across every layer, from classical ML through today's most capable AI systems.
The field will keep moving — new models, new techniques, new applications will keep appearing. What this curriculum aimed to build isn't a snapshot of "what's current," but the durable, underlying understanding that makes each new development legible rather than mysterious.
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