Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
Most developers think large language models were built to predict the next word. They weren't—not at first.
If you travel back to the early 2010s, the hardest problems in AI weren't writing poems or generating code. They were translating English into French, converting speech into text, and summarizing documents. These were all instances of the same challenge: sequence transduction.
The term appears almost casually in the opening paragraph of the Transformer paper:
"...sequence modeling and transduction problems such as language modeling and machine translation."
Today, almost everyone knows the Transformer. Very few remember the problem it was invented to solve.
Ironically, solving sequence transduction turned out to create the foundation upon which modern LLMs would later emerge.
Let's explore why.
Imagine you own a factory.
A sequence modeling problem asks:
"Given everything that has happened so far, what comes next?"
Like predicting the next product coming off the conveyor belt.
The cat sat on the _____
↓
mat
This is language modeling.
A sequence transduction problem is larger idea.
Instead of predicting one missing piece, you transform an entire sequence into another.
English
↓
"The weather is nice."
↓
French
"Il fait beau."
Or
Audio
↓
Waveform
↓
Text
"Welcome everyone."
Or
Buggy code
↓
Correct code
Different input.
Different output.
Often different lengths.
The model must understand the entire source or at least large parts of it before generating the target.
In hindsight, modern AI assistants spend almost all of their time doing sequence transduction:
They all reduce to:
Input sequence → Output sequence
Humans underestimate how much memory translation requires.
Consider translating:
"The committee, after reviewing several proposals over three months, finally approved the budget."
Suppose you're translating into German.
The verb may not appear until the end.
To translate correctly, the model must remember:
Early neural networks processed text one word at a time.
Word₁ → hidden state
↓
Word₂ → hidden state
↓
Word₃ → hidden state
Everything had to be compressed into one hidden vector.
It was like asking someone to summarize an entire novel using only one sticky note.
Eventually information disappears.
This became known as the long-range dependency problem.
During the late 1980s and 1990s, researchers developed Recurrent Neural Networks (RNNs) to process sequential data.
Unlike ordinary neural networks, RNNs reused the same parameters at every time step.
Instead of building a different network for every word, one network repeatedly updated an internal memory.
Mathematically:
hidden_state = f(previous_hidden_state, current_input)
The same computation runs repeatedly.
This parameter sharing was elegant.
Suppose an RNN contains one million parameters.
A thousand-word paragraph still uses one million parameters—not a billion.
The network simply reuses them.
Economically, this was attractive. But computationally, it was painful.
Everything had to happen sequentially.
Word 500 could not begin until word 499 finished.
No parallelism. No GPUs in picture. Training was slow.
In 1997, Sepp Hochreiter and Jürgen Schmidhuber introduced one of the most influential ideas in deep learning:
Long Short-Term Memory (LSTM).
Instead of blindly overwriting memory every step, the network learned gates.
Think of memory like a whiteboard.
Each word asks three questions:
Those questions became three learned gates.
Forget gate.
Input gate.
Output gate.
Instead of forcing every piece of information through the same bottleneck, the model learned what deserved long-term storage.
A surprisingly intuitive analogy is human note-taking.
Most conversations are forgotten.
A few facts are written into your notebook.
LSTMs learned which facts deserved the notebook.
For over a decade, LSTMs dominated speech recognition, handwriting recognition, language translation, and time-series forecasting.
Google, Apple, Microsoft, and Baidu all deployed enormous production systems powered by them.
Around 2014, another breakthrough appeared.
Instead of using one RNN/LSTMs for everything, researchers separated the task into two parts.
Input sentence
↓
Encoder
↓
Meaning vector
↓
Decoder
↓
Output sentence
This architecture became known as the sequence-to-sequence (Seq2Seq) model.
For the first time, neural networks learned translation end-to-end.
No phrase tables or handcrafted grammar or brittle rules. It was just millions of examples.
One famous anecdote came from Google.
Traditional statistical machine translation systems consisted of dozens of independently engineered components accumulated over years.
Neural machine translation replaced much of that complexity with a single differentiable model trained from data. In 2016, Google reported that its neural system substantially reduced translation errors across multiple language pairs while simplifying the overall pipeline.
This represented an engineering improvement for sure, but more importantly it was a philosophical shift.
Instead of programming language knowledge -- we trained it.
The Seq2Seq model still had one weakness.
Everything had to fit inside one vector.
Information gets lost.
In 2014, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio proposed attention.
Instead of remembering everything, just look back whenever necessary.
While generating each output word, the decoder asks:
Which input words matter right now?
Not every word.
Only the relevant ones.
Translation suddenly became much easier.
Long sentences improved dramatically.
The Transformer paper in 2017 -- instead of improving recurrent networks, removed recurrence entirely.
Every word could attend directly to every other word.
Parallel computation became possible.
Training speed increased enormously.
GPUs became dramatically more efficient because every token in a sequence could be processed simultaneously rather than one after another.
Even more interesting was the economics.
Suppose translating a sentence of 100 words with an RNN requires roughly 100 sequential computation steps.
A Transformer still performs similar amounts of arithmetic overall, but many of those operations can execute in parallel on modern accelerators.
The wall-clock training time drops dramatically because GPUs are optimized for large batches of matrix multiplications rather than long chains of sequential dependencies.
That operational advantage—not merely higher accuracy—made scaling practical.
The remarkable twist is that the architecture built to solve translation generalized astonishingly well.
Replace:
English → French
with
Question → Answer
or
Code → Documentation
or
Prompt → Python program
The underlying problem barely changes.
It remains sequence transduction.
Modern LLMs still perform next-token prediction during training.
But from a developer's perspective, they are universal transduction engines.
Every prompt is transformed into another sequence.
The interface changed.
The underlying abstraction survived.
The history of AI is often told as a story about predicting the next word.
That story is incomplete.
For decades, researchers wrestled with a harder question:
How do we transform one complex sequence into another while preserving meaning?
That single question drove the invention of encoder–decoder architectures, LSTMs, attention mechanisms, and ultimately the Transformer itself.
The next time you ask an LLM to refactor code, summarize a meeting, or generate a SQL query, remember what it's really doing.
Not merely predicting words.
Performing sequence transduction at an extraordinary scale.
What surprised you most about this history? Did you always think LLMs grew out of language modeling, or is it more useful to think of them as the latest—and perhaps most powerful—generation of sequence transduction systems?
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
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GenAI today is a race car without brakes. It accelerates fast -- you describe something, and large blocks of code appear instantly. But AI agents silently break things: they remove logic, relax constraints, introduce expensive cloud calls, leak credentials, and change behavior -- without telling you. You often find out in production.
** git-lrc is your braking system.** It hooks into
git commit
and runs an AI review on every diff In short, git-lrc helps Prevent Outages, Breaches, and Technical Debt Before They Happen
At a glance: 10 risk categories · 100+ failure patterns tracked · every commit…