diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml
index 1b7f52cd2fff..30e88341df01 100644
--- a/utils/tuning/config-example.yaml
+++ b/utils/tuning/config-example.yaml
@@ -5,7 +5,39 @@ general:
     do_alsc_colour: 1
     luminance_strength: 0.5
   awb:
-    greyworld: 0
+    # Algorithm can be either 'grey' or 'bayes'
+    algorithm: bayes
+    # priors is only used for the bayes algorithm
+    priors:
+      - lux: 0
+        ct: [ 2000, 13000]
+        probability: [ 1.0, 1.0 ]
+    AwbMode:
+      AwbAuto:
+        lo: 2500
+        hi: 8000
+      AwbIncandescent:
+        lo: 2500
+        hi: 3000
+      AwbTungsten:
+        lo: 3000
+        hi: 3500
+      AwbFluorescent:
+        lo: 4000
+        hi: 4700
+      AwbIndoor:
+        lo: 3000
+        hi: 5000
+      AwbDaylight:
+        lo: 5500
+        hi: 6500
+      AwbCloudy:
+        lo: 6500
+        hi: 8000
+      # One custom mode can be defined if needed
+      #AwbCustom:
+      #  lo: 2000
+      #  hi: 1300
   macbeth:
     small: 1
     show: 0
diff --git a/utils/tuning/libtuning/modules/awb/awb.py b/utils/tuning/libtuning/modules/awb/awb.py
index c154cf3b8609..0dc4f59dcb26 100644
--- a/utils/tuning/libtuning/modules/awb/awb.py
+++ b/utils/tuning/libtuning/modules/awb/awb.py
@@ -27,10 +27,14 @@ class AWB(Module):
 
         imgs = [img for img in images if img.macbeth is not None]
 
-        gains, _, _ = awb(imgs, None, None, False)
-        gains = np.reshape(gains, (-1, 3))
+        ct_curve, transverse_pos, transverse_neg = awb(imgs, None, None, False)
+        ct_curve = np.reshape(ct_curve, (-1, 3))
+        gains = [{
+            'ct': int(v[0]),
+            'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
+        } for v in ct_curve]
+
+        return {'colourGains': gains,
+                'transversePos': transverse_pos,
+                'transverseNeg': transverse_neg}
 
-        return [{
-                    'ct': int(v[0]),
-                    'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
-                } for v in gains]
diff --git a/utils/tuning/libtuning/modules/awb/rkisp1.py b/utils/tuning/libtuning/modules/awb/rkisp1.py
index 0c95843b83d3..d562d26eb8cc 100644
--- a/utils/tuning/libtuning/modules/awb/rkisp1.py
+++ b/utils/tuning/libtuning/modules/awb/rkisp1.py
@@ -6,9 +6,6 @@
 
 from .awb import AWB
 
-import libtuning as lt
-
-
 class AWBRkISP1(AWB):
     hr_name = 'AWB (RkISP1)'
     out_name = 'Awb'
@@ -20,8 +17,20 @@ class AWBRkISP1(AWB):
         return True
 
     def process(self, config: dict, images: list, outputs: dict) -> dict:
-        output = {}
-
-        output['colourGains'] = self.do_calculation(images)
+        if not 'awb' in config['general']:
+            raise ValueError('AWB configuration missing')
+        awb_config = config['general']['awb']
+        algorithm = awb_config['algorithm']
+
+        output = {'algorithm': algorithm}
+        data = self.do_calculation(images)
+        if algorithm == 'grey':
+            output['colourGains'] = data['colourGains']
+        elif algorithm == 'bayes':
+            output['AwbMode'] = awb_config['AwbMode']
+            output['priors'] = awb_config['priors']
+            output.update(data)
+        else:
+            raise ValueError(f"Unknown AWB algorithm {output['algorithm']}")
 
         return output