Case Study 1: Inside a Code Generation Request

Tracing What Happens When a Developer Asks Claude to Write a Sorting Function

The Scenario

Sarah is a developer building a data analysis tool. She has a list of dictionaries representing sales records, and she needs a function to sort them by any given key. She opens her AI coding assistant and types:

Write a Python function called sort_records that takes a list of dictionaries and a key name,
and returns the list sorted by that key. Handle the case where some dictionaries might be
missing the key. Include type hints and a docstring.

Let us trace exactly what happens from the moment Sarah presses Enter to the moment she sees the completed function on her screen.

Step 1: Tokenization (Microseconds)

The first thing that happens is tokenization. Sarah's prompt is converted from human-readable text into a sequence of tokens -- the fundamental units the model processes.

Her prompt is approximately 45 words, which translates to roughly 60-70 tokens. The tokenizer processes the text using its vocabulary, which was built during training using Byte Pair Encoding. Here is an approximation of how the first part tokenizes:

Notice several things: - Common words like "a" and "that" are single tokens - The function name sort_records is split into three tokens because the underscore-separated name is not common enough to warrant a single token - Leading spaces are often attached to the following word token

Each token maps to a unique integer in the model's vocabulary. "Write" might map to token ID 8144, " a" to token ID 264, " Python" to token ID 11361, and so on. These integer IDs are what actually flow into the neural network.

The tokenization step is deterministic and nearly instantaneous -- it involves simple dictionary lookups and pattern matching, with no neural network computation.

Step 2: Embedding (Microseconds)

Each token ID is converted into a high-dimensional vector called an embedding. If the model uses 4,096-dimensional embeddings, then each token becomes a list of 4,096 numbers.

These embeddings are not arbitrary. During training, the model learned that similar tokens should have similar embeddings. The embedding for "list" is closer (in mathematical terms) to the embedding for "array" than to the embedding for "elephant." The embedding for "Python" is closer to "JavaScript" than to "python" (the snake).

Along with the token embeddings, the model adds positional encodings -- additional information that tells the model where each token appears in the sequence. Without positional encoding, the model would not know the difference between "sort the list by key" and "key the by list sort." Position matters, especially in code where the order of statements is crucial.

After this step, Sarah's prompt is represented as a matrix: approximately 65 rows (one per token) and 4,096 columns (the embedding dimension). This matrix is the model's internal representation of her request.

Step 3: Processing Through Transformer Layers (Milliseconds)

Now the core computation begins. The embedded prompt flows through dozens of transformer layers. Each layer has two main components: multi-head attention and a feed-forward neural network.

In the attention phase of the first few layers, the model begins to build basic understanding: - It recognizes "Write a Python function" as a code generation instruction - It links "sort_records" to the concept of sorting - It connects "list of dictionaries" as a data structure specification - It notes "type hints" and "docstring" as quality requirements

As processing moves through middle layers, the model develops a deeper understanding: - It retrieves its knowledge of Python sorting functions, specifically sorted() with a key parameter - It considers the typing module for type hints (since the prompt requests type hints) - It plans for the missing-key edge case, likely considering dict.get() with a default value - It draws on patterns from thousands of similar functions it saw during training

In the later layers, the model crystallizes its plan into a high-level representation: - The overall structure: a function with specific parameters, a docstring, type annotations, and a return statement using sorted() - The edge case handling approach: use a lambda that gracefully handles missing keys - The docstring format and content

Throughout this process, the attention mechanism is doing critical work. When processing the tokens for "missing the key," the attention heads look back at "list of dictionaries" and "key name" to understand exactly what "missing the key" means in this context. When processing "type hints," attention heads reference "list of dictionaries" to determine what types to annotate.

This step involves the vast majority of the computation. For a model with 175 billion parameters and 96 layers, processing 65 input tokens requires trillions of floating-point operations. Modern GPU hardware performs these operations in parallel, completing the entire forward pass in tens to hundreds of milliseconds.

Step 4: Generating the First Token (Milliseconds)

After processing the entire input prompt, the model is ready to generate output. It produces a probability distribution over its entire vocabulary (typically 50,000-100,000 tokens) for the first output token.

Given that Sarah's prompt is clearly requesting a Python function, the probability distribution is heavily concentrated:

The model is very confident that the response should start with def (beginning a function definition) but also considers that it might start with an import statement (from typing import...), a docstring, or a comment.

With typical temperature settings for code generation (around 0.2-0.4), the sampling process almost certainly selects def. This token is appended to the sequence, and the model moves to the next position.

Step 5: Token-by-Token Generation (Milliseconds per Token)

Now the model enters its generation loop. At each step, the entire sequence (original prompt plus all generated tokens so far) is processed to predict the next token.

Let us trace the first several tokens:

Position 1: Given the prompt + def, the model predicts the next token. The function name sort_records was specified in the prompt, so attention heavily focuses on "called sort_records." The next token is sort with very high probability.

Position 2: Given def sort, attention returns to the prompt and finds _records. The next token is _ with near certainty.

Position 3: Following def sort_, the next token is records with near certainty.

Position 4: After def sort_records, the next token is ( -- the model knows Python function syntax requires an opening parenthesis.

Now the model needs to decide on parameter names and type hints. This is where the probability distribution becomes more interesting:

Position 5: After (, the model considers parameter names. The prompt says "list of dictionaries," so common choices include: - records (~30%) - data (~20%) - items (~15%) - lst (~10%) - dicts (~8%)

The model selects records (or whichever the sampling picks from the top candidates).

This process continues, token by token, building the function. Here is a simplified trace of the key decision points:

def sort_records(
    records          <- chosen from multiple plausible parameter names
    :                <- syntax, near-certain
    list             <- type hint, prompt specified "list of dictionaries"
    [                <- opening bracket for generic type
    dict             <- prompt specified "dictionaries"
    [                <- key type annotation
    str              <- most common dict key type
    ,                <- syntax
    Any              <- value type, since no specific type mentioned
    ]                <- closing bracket
    ]                <- closing bracket
    ,                <- separating parameters
    key              <- prompt said "key name"
    :                <- syntax
    str              <- key is a string (name of a dictionary key)
    )                <- closing parenthesis (or -> for return type first)

Each token takes a few milliseconds to generate. The entire function, which might be 150-250 tokens, takes roughly 1-3 seconds to generate.

Step 6: Navigating Critical Decision Points

Several moments during generation are particularly interesting because the model faces genuine choices:

Decision: How to handle missing keys?

When the model reaches the point where it needs to implement the missing-key handling, it considers several approaches:

1. Use dict.get(key, default_value) with a default that sorts last
2. Use a try/except KeyError block
3. Use an if/else check within a lambda
4. Filter out dictionaries missing the key, then sort the rest

The model's training data contains examples of all these approaches. The attention mechanism focuses on the prompt's phrasing ("Handle the case where some dictionaries might be missing the key") to guide the choice. The gentle phrasing ("handle the case") suggests graceful handling rather than filtering/removing, making approach 1 (with dict.get()) the most likely choice.

Decision: What default value for missing keys?

If the model chooses dict.get(), it needs a default value. For sorting purposes, missing values need to sort consistently -- either all at the beginning or all at the end. The model might choose: - None with special handling - "" (empty string) for string keys - A tuple-based approach for flexible sorting - float('inf') for numeric values

This is a genuinely tricky design decision, and the model's choice depends on which patterns were most common in its training data and how the attention mechanism interprets the context.

Decision: Docstring format

The prompt requests a docstring but does not specify the format. The model might use: - Google-style docstrings - NumPy-style docstrings - Sphinx/reStructuredText-style docstrings - Simple one-line docstrings

The choice often depends on which format was most prevalent in the training data for similar functions. Google-style is very common in modern Python, so it often wins.

Step 7: The Complete Output

After approximately 200 tokens of generation, the model produces something like:

def sort_records(
    records: list[dict[str, any]],
    key: str,
    reverse: bool = False,
    default: any = None,
) -> list[dict[str, any]]:
    """Sort a list of dictionaries by a specified key.

    Handles cases where some dictionaries may be missing the specified key
    by using a default value for sorting purposes.

    Args:
        records: A list of dictionaries to sort.
        key: The dictionary key to sort by.
        reverse: If True, sort in descending order. Defaults to False.
        default: The default value to use for sorting when a dictionary
            is missing the specified key. Defaults to None.

    Returns:
        A new list of dictionaries sorted by the specified key.

    Raises:
        TypeError: If records is not a list or key is not a string.

    Example:
        >>> data = [{"name": "Alice", "age": 30}, {"name": "Bob"}, {"name": "Charlie", "age": 25}]
        >>> sort_records(data, "age")
        [{'name': 'Bob'}, {'name': 'Charlie', 'age': 25}, {'name': 'Alice', 'age': 30}]
    """
    if not isinstance(records, list):
        raise TypeError(f"Expected list, got {type(records).__name__}")
    if not isinstance(key, str):
        raise TypeError(f"Expected str for key, got {type(key).__name__}")

    return sorted(records, key=lambda record: record.get(key, default), reverse=reverse)

Notice several things the model added beyond what Sarah explicitly requested: - A reverse parameter (common in sorting functions, the model learned this from patterns) - A default parameter (makes the missing-key handling configurable) - Error handling with type checks (RLHF training emphasized defensive coding) - A comprehensive docstring with an example (fine-tuning examples demonstrated this quality) - A return type annotation (completing the type hints requirement)

Step 8: Output Decoding (Microseconds)

The final step is trivial: the sequence of token IDs is mapped back to text strings using the tokenizer's vocabulary, and the text is displayed to Sarah.

What Sarah Should Do Next

Understanding the generation process helps Sarah evaluate the output critically:

1. Verify the core logic: The sorted() call with lambda record: record.get(key, default) is correct and handles missing keys gracefully. But she should consider: is None an appropriate default for all sorting scenarios? Sorting mixed types (values and None) can raise TypeError in Python 3.

2. Check the extras: The model added reverse and default parameters she did not request. Are they useful? In this case, yes -- they make the function more flexible. But sometimes the model adds unnecessary complexity.

3. Test edge cases: What happens with an empty list? What about dictionaries where the key exists but the value is None? What about non-comparable value types?

4. Evaluate the docstring: Is the example accurate? Does the output match what the function would actually produce? (In this case, the sorting behavior with None default values depends on the value types and could raise a TypeError.)

5. Consider the context: Does this function match the patterns used elsewhere in her codebase? If she uses NumPy-style docstrings elsewhere, she might want to adjust.

Key Takeaways from This Trace

1. Tokenization is the foundation: The model never sees "words" -- it sees tokens. Understanding this explains many quirks of AI-generated code.

2. Attention is the bridge: The model's ability to connect "missing the key" back to "list of dictionaries" and "key name" is what makes the output coherent. This is attention at work.

3. Every token is a choice: Each generated token involves weighing probabilities. For syntax tokens, the choice is nearly certain. For design decisions, the probabilities are more spread out.

4. Training shapes quality: The comprehensive docstring, type hints, and error handling are products of fine-tuning and RLHF, not just pattern matching.

5. The model adds value -- and assumptions: It added useful parameters and thorough documentation beyond what was requested, but it also made assumptions (like the default value) that should be reviewed.

6. Forward-only generation matters: The model committed to dict.get(key, default) early and built the rest of the function around it. If a different approach would have been better, the model could not go back and change its choice.

Understanding this process does not just satisfy curiosity -- it directly improves how you interact with AI coding assistants. When you know how the sausage is made, you can write better prompts, anticipate failure modes, and evaluate output more critically.