CREATE TABLE "Genre" (
      "GenreId" INTEGER NOT NULL, 
      "Name" NVARCHAR(120), 
      PRIMARY KEY ("GenreId")
    )
    /*
    3 rows from Genre table:
    GenreId	Name
    1	Rock
    2	Jazz
    3	Metal
    */
  sql_cmd: SELECT "Name" FROM "Genre" WHERE "Name" LIKE 'r%';
  sql_result: "[('Rock',), ('Rock and Roll',), ('Reggae',), ('R&B/Soul',)]"
  answer: The genres that start with 'r' are Rock, Rock and Roll, Reggae and R&B/Soul. 
"""
Now that you have some examples (with manually corrected output SQL), you can do few shot prompt seeding the usual way:
from langchain import FewShotPromptTemplate, PromptTemplate
from langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIX
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
from langchain.vectorstores import Chroma
example_prompt = PromptTemplate(
    input_variables=["table_info", "input", "sql_cmd", "sql_result", "answer"],
    template="{table_info}\n\nQuestion: {input}\nSQLQuery: {sql_cmd}\nSQLResult: {sql_result}\nAnswer: {answer}",
)
examples_dict = yaml.safe_load(YAML_EXAMPLES)
local_embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
example_selector = SemanticSimilarityExampleSelector.from_examples(
                        # This is the list of examples available to select from.
                        examples_dict,
rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/sqlite.html