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AI Writing Tropes to Avoid

Add this file to your AI assistant's system prompt or context to help it avoid common AI writing patterns. Source: tropes.fyi by ossama.is

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Word Choice

"Quietly" and Other Magic Adverbs

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Update Python version of existing project using UV

1. Update pyproject.toml

[project]
requires-python = ">=3.11"

2. uv python pin 3.11
3. uv sync
4. Optional: uv pip compile pyproject.toml -o requirements.txt

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def semantic_cached_prompt(prompt):
    """Retrieve a response from ScyllaDB or ask OpenAI if it's a new prompt.
    Args:
        prompt (str): The user prompt.
    Returns:
        str: The response to the prompt.
    """
    embedding = create_embedding(prompt)    
    cached_response = search_cache(embedding, threshold=0.80)
    if cached_response:

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def calc_cosine_similarity(self, vec1, vec2):
  """Calculate cosine similarity between two vectors."""
  v1, v2 = np.array(vec1), np.array(vec2)
  return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))

def search_cache(self, embedding, threshold=0.80):
  """       
  Returns the most similar response if it is above the threshold. Otherwise returns `None`.
  """
  k = 1

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# pip install sentence-transformers
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
embedding = embedding_model.encode(text).tolist()

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CREATE KEYSPACE semantic_cache WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor': '3'};

CREATE TABLE semantic_cache.prompts (
    prompt_id uuid PRIMARY KEY,
    inserted_at timestamp,
    prompt_text text,
    prompt_embedding vector<float, 384>,
    llm_response text,
    updated_at timestamp
);

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CREATE KEYSPACE recommend WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor': '3'};

CREATE TABLE recommend.movies (
    id INT,
    release_date TIMESTAMP,
    title TEXT,
    tagline TEXT,
    genre TEXT,
    imdb_id TEXT,
    poster_url TEXT,

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from db.scylladb import ScyllaClient
from embedding_creator import EmbeddingCreator
from models import Movie
    
class MovieRecommender:
    
    def __init__(self):
        self.scylla_client = ScyllaClient()
        self.embedding_creator = EmbeddingCreator("all-MiniLM-L6-v2")
    

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from sentence_transformers import SentenceTransformer
class EmbeddingCreator:
    def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
        self.embedding_model = SentenceTransformer(model_name, device='cpu')
    
    def create_embedding(self, text: str) -> list[float]:
        """
        Get embedding for a single text input using SentenceTransformer.
        Returns the embedding vector.
        """

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CREATE KEYSPACE ttl_example WITH replication = {'class': 'NetworkTopologyStrategy', 'replication_factor': 1};

USE ttl_example;


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-- BASIC USAGE
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-- Set deafult TTL with CREATE TABLE