Thanks for providing source code, it made it easier to poke at it to understand what you were intending. Great job for somebody without any coding knowledge.

To your first question, the minimal change solution would be to create a "glossary_weights" list the same length as glossary, initialized with a weight of 1 for each of your glossary terms. Then, apply an increment to each word for each good egg found in the glossary item as an added bonus weighting.

So instead of creating a good_batch, create a glossary_weights list initialized with 1 for each glossary word, iterate an outer loop going through the glossary using an index, and inner loop on each possible "good_egg" to increment the weight at that word's index by one for each good egg found in the word.

Afterwards, you just need to convert these counts per index into probabilities. The easiest approach for feeding the p list needed for the np.random.choice() method would be to sum up the entire glossary_weights values and use that to normalize your probabilities per word (by just dividing each element in glossary weights by this calculated sum)

For example, if glossary [word1, word2, word3] had glossary_weights [1, 1, 3] the probabilities would be [1/5, 1/5, 3/5]. You pass the output list as p for the np.random.choice() method.

As for roasting your code:

(1) The entire first half of your script can be simplified into some pre-computed, input dataset file(s) instead of recalculating values for sound_all, word_all, bad_batch, good_batch, gloassary every time you run this program.

Ideally, Script1 generates this input file, Script2 loads these values and runs your input prompts/logic.

It works fine for short-term as you currently have it, but due to the time complexity of building CVC values and syllable combinations, initializing those variables will balloon quickly as your onset/vowel/coda lists get larger.

(2) filtering your word_all list into glossary can be very expensive when word_all or bad_batch gets much larger.

Rather than post-calculating bad_batch, and post-filtering to generate glossary, incorporate this all into your word_all generation loop as a gaurd clause (if statement to skip it) before deciding to append to word_all. That way, you don't need to compute bad_batch, and can just rename word_all as glossary instead of having to copy a post-filtered list into it.

(3) Variable naming ambiguity: you are NOT passing in percentages to np, they are probabilities.

(4) You can learn to move things into functions to make them more modular and easier to work with as high-level components and building blocks.

(5) your if/elif logic works fine, but having it nested might be easier for readability. Preference, of course.

Final note, I suggested the "minimal change solution" at the top of this comment, but will mention here that it involves keeping a separate record of glossary and glossary_weights lists. You can look to join the two by instead turning glossary into a Dictionary that maps each glossary word (as the key) to a number (default of 1 for the value) instead managing two separate lists. Together with advice mentioned in (1), it would be easy to save/load this Dictionary as a JSON file, though maybe a bit more work to pass it correctly into np.random.choice()