Recall: Local LLM Memory for Claude Code Interviews

You're grinding for that Staff+ role. You've got your systems design down, you've memorized your LeetCode patterns, and then comes the curveball: a coding interview, but you're pairing with a large language model. Specifically, Claude. And it’s not really pairing if you have to remind it of the function signature you just wrote two turns ago. This isn't just about knowing Python; it's about managing a conversation with an AI that has a notoriously short recall window. We're talking local memory here, folks. If you don't explicitly handle it, Claude will forget your brilliant helper function faster than your cat forgets it just ate.

Trust me, I’ve seen good engineers flounder because they treat the LLM like a human, expecting it to remember context. It won't. This isn't a human interviewer; it's a state machine with a limited-size input buffer. Your job is to make sure the state it needs is always in that buffer.

The Claude Recall Problem: A Hard Truth

Claude, like many LLMs, operates on a fixed context window. Think of it as a notepad with a limited number of lines. Each interaction, each line of code, each instruction you give, takes up space. When that notepad fills up, the oldest stuff gets pushed out. This means if you write a User class, then a Product class, and then twenty lines of database interaction, Claude might totally forget User existed unless you explicitly re-introduce it. This is especially brutal in coding interviews because you're building up a solution incrementally. You define a data structure, then a helper function, then the main logic. If Claude loses the data structure definition, it'll start hallucinating about Product having a customer_id field it never had.

This isn't a critique of Claude; it's a technical reality. What’s critical is how you adapt to it. Your human interviewer understands the whole conversation. Claude doesn’t. It only understands what’s currently in its window.

Strategies for Explicit Context Management

The core idea is to always provide the necessary context with each prompt. This sounds tedious, and it absolutely can be, but it’s the difference between a successful interview and one where you spend half your time correcting the LLM.

First, keep track of the relevant code snippets yourself. I open a scratchpad (VS Code, Notepad++, whatever) and paste every significant piece of code I write. This isn't just for Claude; it helps me organize my thoughts too.

When you ask Claude to, say, implement a new method for an existing class, don't just say, "Add a get_total_price method to the Order class." Instead, provide the skeleton:

class Order:
    def __init__(self, items):
        self.items = items # Assume items is a list of (product, quantity) tuples

    # Existing methods Claude needs to remember
    def calculate_subtotal(self):
        # ... logic ...
        pass

    # Your request starts here
    def get_total_price(self):
        # ... your new logic ...
        pass

This takes up more tokens, yes, but it ensures Claude has the full definition of Order it needs to reason about items and calculate_subtotal. Without it, Claude might invent an Order.price attribute that doesn't exist, or forget calculate_subtotal entirely.

"Recall Blocks": Your Secret Weapon

I call these "recall blocks." They’re clearly delineated sections of crucial information you paste into your prompt before your actual question or instruction.

A good recall block might look like this:

<recall>
# Current Code State:
class User:
    def __init__(self, user_id: str, username: str):
        self.user_id = user_id
        self.username = username
        self.orders = []

    def add_order(self, order_id: str, items: list[tuple[str, int]]):
        # ... implementation detail, assume it works ...
        pass

class Order:
    def __init__(self, order_id: str, user_id: str, items: list[tuple[str, int]]):
        self.order_id = order_id
        self.user_id = user_id
        self.items = items # e.g., [('apple', 2), ('banana', 1)]

    def get_total_items(self) -> int:
        return sum(qty for _, qty in self.items)
</recall>

Now, please write a new function, `find_user_orders(user_id: str) -> list[Order]`, which iterates through a global list of all orders and returns only those belonging to the given `user_id`. You can assume `ALL_ORDERS: list[Order]` is globally accessible.

Notice the <recall> tags. These are purely for your benefit, to visually segment the context. Claude doesn't care about the tags, but you will, especially when you're quickly assembling a prompt under pressure. You're explicitly telling Claude, "Hey, remember all this stuff?" It’s like highlighting the important parts of a textbook before asking a question.

Simulating Real Interview Scenarios

Let's walk through a typical coding interview progression with Claude, managing recall.

Scenario: Implement a simple in-memory key-value store with get, set, and delete operations, then add a lru_cache decorator.

1. Initial setup (first prompt):

   I need to implement a simple in-memory key-value store. Let's start with the basic `KeyValueStore` class. It should have an `__init__` method and basic `get(key)`, `set(key, value)`, and `delete(key)` methods. Use a simple dictionary for storage.
   

   Claude will output a basic class. You save this to your scratchpad.

2. Adding LRU functionality (second prompt):

   <recall>
   # Current Code State:
   class KeyValueStore:
       def __init__(self):
           self.store = {}
   
       def get(self, key):
           return self.store.get(key)
   
       def set(self, key, value):
           self.store[key] = value
   
       def delete(self, key):
           if key in self.store:
               del self.store[key]
               return True
           return False
   </recall>
   
   Now, I want to add an LRU cache decorator. This decorator should take a `capacity` argument. When a key is accessed (via `get`) or modified (via `set`), it should move to the most recently used position. If `set` is called and the cache is at capacity, the least recently used item should be evicted before the new item is added. Assume `get` returning `None` (key not found) doesn't count as an access for LRU purposes.
   

   Here, you must provide the KeyValueStore definition. If you don't, Claude might start guessing about how get or set work, or worse, invent a new data structure entirely. It's not guaranteed to remember the previous turn's output if too many tokens have passed. Claude will likely try to modify the KeyValueStore directly or suggest a separate decorator.

3. Refining the LRU implementation (third prompt, if Claude didn't get it right):

   <recall>
   # Current Code State:
   # (Paste Claude's previous output for the LRU decorator and KeyValueStore here,
   # even if it was partially incorrect. You provide the current *state of the code*.)
   
   # Example: Claude's attempt might look like this (partially incorrect):
   from collections import OrderedDict
   
   class KeyValueStore:
       def __init__(self):
           self.store = OrderedDict() # Claude might have changed this
           self.capacity = 10 # And added this
   
       def get(self, key):
           if key in self.store:
               value = self.store.pop(key)
               self.store[key] = value # Move to end
               return value
           return None
   
       def set(self, key, value):
           if key in self.store:
               self.store.pop(key) # Move to end
           elif len(self.store) >= self.capacity:
               self.store.popitem(last=False) # Evict LRU
           self.store[key] = value
   
       def delete(self, key):
           # ... (assume Claude kept this mostly correct) ...
           pass
   </recall>
   
   Actually, I'd prefer the LRU logic to be encapsulated in a separate decorator function, `lru_cache_decorator`, that you can apply to methods of `KeyValueStore`. The `KeyValueStore` itself should remain as simple as possible, just managing the dictionary. The decorator should handle the LRU eviction and access reordering.
   

   This is where it gets tricky. If you just said, "Make it a decorator," Claude might get confused about scope or how to apply it. By providing all the current code, even the parts you want to change, you give it the full context. You're showing Claude, "This is what we have; now transform it this way."

The Token Budget: A Real Constraint

The biggest caveat? Tokens. Each character, each word, each line of code, consumes tokens. LLMs have a maximum context window, often measured in tokens. If your recall blocks become too large – think hundreds of lines of code – you'll hit that limit. Even if you don't hit the hard limit, a larger context window generally means slower responses and potentially higher costs in a real-world scenario (though not usually in an interview setting).

So, you have to be judicious. Don't include every single comment or every print statement. Focus on class definitions, function signatures, and critical logic. If you have multiple small helper functions that aren’t directly involved in the current task, you might just include their signatures or a brief description:

# Helper function (assume implementation is correct and not relevant to current task)
def _validate_input(data: dict) -> bool:
    # ...
    pass

This is a trade-off. You balance the risk of Claude forgetting context against the cost and slowness of providing too much context. For most interview problems, especially those designed for 45-60 minutes, the amount of code shouldn't overwhelm a modern LLM's context window. If it does, the problem itself is probably too complex for the format.

Debugging and Iterating With Recall

Debugging with an LLM is a skill in itself. When Claude generates incorrect code, don't just say, "That's wrong." Point to the specific error, and crucially, provide the full current code state again.

<recall>
# Current Code State:
# (Paste the full, erroneous code from Claude here)
</recall>

The `find_user_orders` function you provided has a bug. It's trying to access `order.user_id` but `Order` objects don't have that attribute directly. It should access `order.customer_id` instead. Please correct this.

Always give Claude the complete picture. It's like a compiler that forgets your previous compile flags each time you hit "compile." You have to re-specify everything. This approach feels less "natural" than talking to a human, but it’s how you get predictable, effective results from an LLM.

Beyond Code: Remembering Requirements

Recall isn't just for code. It's for requirements too. If the interviewer told you, "All IDs must be UUIDs," and then ten turns later you're implementing a search function, Claude might default to integer IDs. If that's a critical constraint, occasionally remind it:

<recall>
# Key Requirements:
- All user IDs and order IDs must be UUID strings.
- All prices should be represented as integers in cents to avoid floating point issues.
</recall>

Now, implement the `get_order_summary(order_id: str)` function. Remember to use integer cents for any price calculations.

This works particularly well for long-running interviews where constraints might get lost over time. A quick Key Requirements block can save you from a trivial logical error that an LLM might otherwise introduce.

Your Role: LLM Conductor, Not Just Coder

When you’re in a Claude-assisted coding interview, you're not just a coder. You're an LLM conductor. Your primary job becomes:

1. Breaking down the problem: Just like you would for a human, but with even smaller, more explicit steps.
2. Maintaining context: This is where recall blocks shine. Ensure Claude always has the necessary information.
3. Guiding and correcting: When Claude falters, provide precise, actionable feedback, always with the relevant code snippet.

This is a new skillset. It’s not just about writing code; it’s about managing an AI to write code. Embrace it. The engineers who master this will have a distinct edge in these evolving interview formats.

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