Why Text embedding models in Prompt Engineering / GenAI? - Purpose & Use Cases

The Big Idea

What if your computer could understand the meaning behind your words instantly?

The Scenario

Imagine you have thousands of documents and you want to find which ones are similar or about the same topic. Doing this by reading each document and comparing them word by word would take forever.

The Problem

Manually checking text similarity is slow and tiring. It's easy to miss connections because words can have many meanings. Also, comparing long texts by hand leads to mistakes and inconsistent results.

The Solution

Text embedding models turn words and sentences into numbers that capture their meaning. This lets computers quickly compare texts by looking at these numbers, finding similarities even if the words are different but the meaning is close.

Before vs After

✗ Before

for doc1 in docs:
    for doc2 in docs:
        if doc1 != doc2:
            # manually check word overlap or keywords
            compare_texts(doc1, doc2)

✓ After

embeddings = model.embed(docs)
similarities = compute_similarity(embeddings)

What It Enables

It makes understanding and comparing large amounts of text fast, accurate, and scalable, unlocking powerful search and recommendation tools.

Real Life Example

When you search for a product online, text embedding models help find items with similar descriptions or reviews, even if you use different words than the seller.

Key Takeaways

Manual text comparison is slow and error-prone.

Text embedding models convert text into meaningful numbers.

This enables fast and smart text similarity and search.

Practice

1. What is the main purpose of a text embedding model?

easy

A. To convert text into numbers that capture its meaning

B. To translate text from one language to another

C. To generate images from text descriptions

D. To count the number of words in a text

Solution

1. Step 1: Understand what text embedding models do

   Text embedding models turn words or sentences into number arrays that represent their meaning.

2. Step 2: Compare options with this understanding

   Only To convert text into numbers that capture its meaning describes converting text into meaningful numbers. Other options describe different tasks.

3. Final Answer:

   To convert text into numbers that capture its meaning -> Option A

4. Quick Check:

   Text embedding = convert text to meaningful numbers [OK]

Hint: Remember: embeddings turn text into numbers for meaning [OK]

Common Mistakes:

• Confusing embeddings with translation
• Thinking embeddings generate images
• Assuming embeddings just count words

2. Which of the following is the correct way to get an embedding vector from a text using a Python function get_embedding(text)?

easy

A. embedding = get_embedding->text

B. embedding = get_embedding[text]

C. embedding = get_embedding{text}

D. embedding = get_embedding(text)

Solution

1. Step 1: Recall Python function call syntax

   In Python, functions are called with parentheses and arguments inside, like func(arg).

2. Step 2: Match syntax with options

   Only embedding = get_embedding(text) uses parentheses correctly. Options A, B, and C use invalid syntax for function calls.

3. Final Answer:

   embedding = get_embedding(text) -> Option D

4. Quick Check:

   Function call uses parentheses () [OK]

Hint: Use parentheses () to call functions in Python [OK]

Common Mistakes:

• Using square brackets [] instead of parentheses
• Using curly braces {} instead of parentheses
• Using arrow -> instead of parentheses

3. Given the code below, what will be the output?

def dummy_embedding(text):
    return [len(text), sum(ord(c) for c in text) % 100]

result = dummy_embedding('cat')
print(result)

medium

A. [3, 12]

B. [3, 15]

C. [4, 30]

D. [3, 30]

Solution

1. Step 1: Calculate length of 'cat'

   The word 'cat' has 3 characters, so first element is 3.

2. Step 2: Calculate sum of ASCII codes modulo 100

   ord('c')=99, ord('a')=97, ord('t')=116; sum=99+97+116=312; 312 % 100 = 12.

3. Step 3: Determine output

   return [3, 12], so print([3, 12]).

4. Final Answer:

   [3, 12] -> Option A

5. Quick Check:

   len('cat')=3, (99+97+116)%100=12 [OK]

Hint: Calculate length and ASCII sum mod 100 carefully [OK]

Common Mistakes:

• Wrong ASCII sum calculation
• Miscounting string length
• Mixing uppercase and lowercase ASCII codes

4. The following code tries to get embeddings for two texts but doesn't work as intended. What is the problem?

def get_embedding(text):
    return [len(text)]

texts = ['hello', 'world']
embeddings = []
for t in texts:
    embeddings.append(get_embedding)
print(embeddings)

medium

A. The list texts is empty

B. The function is not called; it appends the function itself

C. The variable embeddings is not defined

D. The function get_embedding has wrong syntax

Solution

1. Step 1: Check the loop appending embeddings

   The code appends get_embedding without parentheses, so it adds the function object, not the result.

2. Step 2: Understand the problem

   Appending the function itself causes the list to hold function references, not embedding lists like [5] and [5].

3. Final Answer:

   The function is not called; it appends the function itself -> Option B

4. Quick Check:

   Missing () calls function, else appends function object [OK]

Hint: Add () to call function, not just reference it [OK]

Common Mistakes:

• Forgetting parentheses to call function
• Assuming list is empty causes error
• Thinking variable is undefined

5. You want to find the most similar sentence to 'I love apples' from a list using embeddings. Which approach is best?

hard

A. Count common words between 'I love apples' and each sentence

B. Translate all sentences to another language and compare lengths

C. Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding

D. Randomly pick a sentence from the list

Solution

1. Step 1: Understand similarity with embeddings

   Embeddings turn sentences into number arrays capturing meaning, so comparing distances between embeddings finds similar sentences.

2. Step 2: Evaluate options for similarity search

   Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding uses embeddings and distance, which is the correct method. Options A, C, and D do not use embeddings or meaningful similarity measures.

3. Final Answer:

   Compute embeddings for all sentences, then find the one with smallest distance to 'I love apples' embedding -> Option C

4. Quick Check:

   Use embeddings + distance for similarity [OK]

Hint: Use embedding distances to find similar texts [OK]

Common Mistakes:

• Using word count instead of embeddings
• Ignoring embeddings for similarity
• Random selection instead of comparison